diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt
index 7d427431db..47bb582c30 100644
--- a/tools/server/CMakeLists.txt
+++ b/tools/server/CMakeLists.txt
@@ -17,6 +17,8 @@ add_library(${TARGET} STATIC
     server-context.h
     server-tools.cpp
     server-tools.h
+    server-schema.cpp
+    server-schema.h
 )
 
 if (BUILD_SHARED_LIBS)
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 31280d63c4..aebca306a8 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -4,6 +4,7 @@
 #include "server-http.h"
 #include "server-task.h"
 #include "server-queue.h"
+#include "server-schema.h"
 
 #include "build-info.h"
 #include "common.h"
@@ -3825,7 +3826,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
             task.id = rd.get_new_id();
 
             task.tokens = std::move(inputs[i]);
-            task.params = server_task::params_from_json_cmpl(
+            task.params = server_schema::eval_llama_cmpl_schema(
                     ctx_server.vocab,
                     params,
                     meta->slot_n_ctx,
diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp
new file mode 100644
index 0000000000..d5d747a654
--- /dev/null
+++ b/tools/server/server-schema.cpp
@@ -0,0 +1,635 @@
+#include "server-schema.h"
+
+#include "json-schema-to-grammar.h"
+
+namespace server_schema {
+
+//
+// llama.cpp-specific completion schema
+//
+
+std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params & params_base, task_params & params) {
+    std::vector<std::unique_ptr<field>> fields;
+    auto add = [&](field * f) {
+        fields.emplace_back(f);
+    };
+
+    add((new field_bool("timings_per_token", params.timings_per_token))
+        ->set_desc("Include prompt processing and text generation speed information in each response"));
+
+    add((new field_bool("stream", params.stream))
+        ->set_desc("Allows receiving each predicted token in real-time instead of waiting for the completion to finish"));
+
+    add((new field_nested("stream_options"))
+        ->add_subfield((new field_bool("include_usage", params.include_usage))
+            ->set_desc("Whether to include usage information in the stream"))
+        ->set_desc("Additional options for streaming responses"));
+
+    add((new field_bool("cache_prompt", params.cache_prompt))
+        ->set_desc("Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests"));
+
+    add((new field_bool("return_tokens", params.return_tokens))
+        ->set_desc("Return the raw generated token ids in the `tokens` field"));
+
+    add((new field_bool("return_progress", params.return_progress))
+        ->set_desc("Include prompt processing progress events in stream mode"));
+
+    add((new field_num("n_predict", params.n_predict))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->add_alias("max_completion_tokens")
+        ->add_alias("max_tokens")
+        ->set_desc("Set the maximum number of tokens to predict. When 0, no tokens will be generated but the prompt is evaluated into the cache"));
+
+    add((new field_num("n_indent", params.n_indent))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks"));
+
+    add((new field_num("n_keep", params.n_keep))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("Specify the number of tokens from the initial prompt to retain when context size is exceeded. Use -1 to retain all tokens from the prompt"));
+
+    add((new field_num("n_discard", params.n_discard))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Number of tokens after n_keep that may be discarded when shifting context (0 = half context)"));
+
+    add((new field_num("n_cmpl", params.n_cmpl))
+        ->set_hard_limits(1, params_base.n_parallel)
+        ->add_alias("n") // alias "n" as fallback (OpenAI completions API)
+        ->set_desc("Number of completions to generate. If the input has multiple prompts, total outputs will be N prompts times n_cmpl"));
+
+    add((new field_num("n_cache_reuse", params.n_cache_reuse))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Min chunk size to attempt reusing from the cache via KV shifting. See --cache-reuse arg"));
+
+    // TODO: implement t_max_prompt_ms
+    // add((new field_num("t_max_prompt_ms", params.t_max_prompt_ms))
+
+    add((new field_num("t_max_predict_ms", params.t_max_predict_ms))
+        ->set_hard_limits(-1, std::numeric_limits<int64_t>::max())
+        ->set_desc("Set a time limit in milliseconds for the prediction phase. The timeout triggers if generation exceeds this time (measured since the first token) and a newline has been generated. Useful for FIM applications"));
+
+    add((new field_json("response_fields"))
+        ->set_desc("A list of response fields to return. Missing fields are omitted without error. Fields with a slash are unnested (e.g. generation_settings/n_predict moves n_predict to the root)")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.response_fields = json_value(data, "response_fields", std::vector<std::string>());
+        }));
+
+
+    //
+    // Sampling params
+    //
+
+    add((new field_num("top_k", params.sampling.top_k))
+        ->set_limits(0, INT32_MAX)
+        ->set_desc("Limit the next token selection to the K most probable tokens (0 = disabled)"));
+
+    add((new field_num("top_p", params.sampling.top_p))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("Limit the next token selection to a subset of tokens with cumulative probability above threshold P (1.0 = disabled)"));
+
+    add((new field_num("min_p", params.sampling.min_p))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("The minimum probability for a token to be considered, relative to the probability of the most likely token (0 = disabled)"));
+
+    add((new field_num("top_n_sigma", params.sampling.top_n_sigma))
+        ->set_desc("Keep tokens within n standard deviations of the top token logit (< 0 = disabled)"));
+
+    add((new field_num("xtc_probability", params.sampling.xtc_probability))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("Set the chance for token removal via XTC sampler (0 = disabled)"));
+
+    add((new field_num("xtc_threshold", params.sampling.xtc_threshold))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("Set a minimum probability threshold for tokens to be removed via XTC sampler (> 0.5 disables XTC)"));
+
+    add((new field_num("typical_p", params.sampling.typ_p))
+        // ->set_limits(0.0f, 1.0f) // what's the valid range?
+        ->set_desc("Enable locally typical sampling with parameter p (1.0 = disabled)"));
+
+    add((new field_num("temperature", params.sampling.temp))
+        ->set_limits(0.0f, std::numeric_limits<float>::infinity())
+        ->set_desc("Adjust the randomness of the generated text (0 = greedy)"));
+
+    add((new field_num("dynatemp_range", params.sampling.dynatemp_range))
+        ->set_desc("Dynamic temperature range. The final temperature will be in [temperature - range, temperature + range] (0 = disabled)"));
+
+    add((new field_num("dynatemp_exponent", params.sampling.dynatemp_exponent))
+        ->set_desc("Dynamic temperature exponent, controls how entropy maps to temperature"));
+
+    add((new field_num("repeat_last_n", params.sampling.penalty_last_n))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("Last n tokens to consider for penalizing repetition (0 = disabled, -1 = ctx-size)"));
+
+    add((new field_num("repeat_penalty", params.sampling.penalty_repeat))
+        ->set_desc("Control the repetition of token sequences in the generated text (1.0 = disabled)"));
+
+    add((new field_num("frequency_penalty", params.sampling.penalty_freq))
+        ->set_desc("Repeat alpha frequency penalty (0 = disabled)"));
+
+    add((new field_num("presence_penalty", params.sampling.penalty_present))
+        ->set_desc("Repeat alpha presence penalty (0 = disabled)"));
+
+    add((new field_num("dry_multiplier", params.sampling.dry_multiplier))
+        ->set_desc("Set the DRY (Don't Repeat Yourself) repetition penalty multiplier (0 = disabled)"));
+
+    add((new field_num("dry_base", params.sampling.dry_base))
+        ->set_desc("Set the DRY repetition penalty base value (must be >= 1.0, any values < 1.0 will be replaced with the default value)")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            float v = data.at("dry_base").get<float>();
+            ctx.params.sampling.dry_base = (v < 1.0f) ? params_base.sampling.dry_base : v;
+        }));
+
+    add((new field_num("dry_allowed_length", params.sampling.dry_allowed_length))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Tokens that extend repetition beyond this length receive exponentially increasing penalty: multiplier * base ^ (sequence_length - allowed_length)"));
+
+    add((new field_num("dry_penalty_last_n", params.sampling.dry_penalty_last_n))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("How many tokens to scan for repetitions (0 = disabled, -1 = context size)"));
+
+    add((new field_num("mirostat", params.sampling.mirostat))
+        ->set_limits(0, 2)
+        ->set_desc("Enable Mirostat sampling, controlling perplexity during text generation (0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"));
+
+    add((new field_num("mirostat_tau", params.sampling.mirostat_tau))
+        ->set_desc("Set the Mirostat target entropy, parameter tau"));
+
+    add((new field_num("mirostat_eta", params.sampling.mirostat_eta))
+        ->set_desc("Set the Mirostat learning rate, parameter eta"));
+
+    add((new field_num("adaptive_target", params.sampling.adaptive_target))
+        ->set_limits(-std::numeric_limits<float>::max(), 1.0f)
+        ->set_desc("Adaptive sampling target entropy (valid range 0.0 to 1.0; negative = disabled)"));
+
+    add((new field_num("adaptive_decay", params.sampling.adaptive_decay))
+        ->set_hard_limits(0.0f, 0.99f)
+        ->set_desc("EMA decay for adaptive sampling; history approximates 1/(1-decay) tokens"));
+
+    // seed is uint32_t; field_num uses int32_t so use a handler
+    add((new field_num("seed", params.sampling.seed))
+        ->set_desc("Set the random number generator (RNG) seed (-1 = random)"));
+
+    add((new field_num("n_probs", params.sampling.n_probs))
+        ->add_alias("logprobs") // use "logprobs" if "n_probs" wasn't provided
+        ->set_desc("If greater than 0, output the probabilities of top N tokens for each generated token"));
+
+    add((new field_num("min_keep", params.sampling.min_keep))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("If greater than 0, force samplers to return at least N possible tokens"));
+
+    add((new field_bool("backend_sampling", params.sampling.backend_sampling))
+        ->set_desc("Use backend sampling instead of llama.cpp sampling"));
+
+    add((new field_bool("post_sampling_probs", params.post_sampling_probs))
+        ->set_desc("Return probabilities of top n_probs tokens after applying the sampling chain"));
+
+    //
+    // Speculative decoding params
+    //
+
+    // TODO: to keep things simple, we disable speculative parameter adjustments for now
+#if 0
+    // TODO: for now, be able to adjust only the draft-model based speculative parameters
+    add((new field_num("speculative.n_max", params.speculative.draft.n_max))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Maximum number of tokens to draft during speculative decoding"));
+
+    add((new field_num("speculative.n_min", params.speculative.draft.n_min))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Minimum number of draft tokens to use for speculative decoding");
+
+    add((new field_num("speculative.p_min", params.speculative.draft.p_min))
+        ->set_hard_limits(0.0f, 1.0f)
+        ->set_desc("Minimum speculative decoding probability for draft tokens (0 = greedy)"));
+
+    add((new field_str("speculative.type"))
+        ->set_desc("Speculative decoding method (for debugging and research purposes)")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.speculative.types = { common_speculative_type_from_name(data.at("speculative.type").get<std::string>()) };
+        }));
+
+    add((new field_num("speculative.ngram_size_n", params.speculative.ngram_simple.size_n))
+        ->set_desc("Ngram size for lookup in ngram-based speculative decoding"));
+
+    add((new field_num("speculative.ngram_size_m", params.speculative.ngram_simple.size_m))
+        ->set_desc("Mgram size for speculative tokens in ngram-based speculative decoding"));
+
+    add((new field_num("speculative.ngram_min_hits", params.speculative.ngram_simple.min_hits))
+        ->set_desc("Minimum hits at ngram lookup for mgram to be proposed"));
+#endif
+
+    add((new field_json("lora"))
+        ->set_desc("A list of LoRA adapters to apply to this request. Each entry must have `id` and `scale` fields. Adapters not listed default to scale 0.0")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            const auto & lora = data.at("lora");
+            if (!lora.is_array()) {
+                throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
+            }
+            ctx.params.lora = parse_lora_request(lora);
+        }));
+
+    // sequence breakers for DRY
+    // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
+    // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
+    add((new field_json("dry_sequence_breakers"))
+        ->set_desc("Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
+            if (ctx.params.sampling.dry_sequence_breakers.empty()) {
+                throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
+            }
+        }));
+
+    // handle both "json_schema" and "grammar"
+    add((new field_json("json_schema"))
+        ->add_alias("grammar")
+        ->set_desc("Set a JSON schema (json_schema) or GBNF grammar string (grammar) for constrained generation. json_schema takes precedence if both are provided")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            auto & params = ctx.params;
+            if (data.contains("json_schema") && !data.contains("grammar")) {
+                try {
+                    auto schema                  = json_value(data, "json_schema", json::object());
+                    SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
+                    std::string grammar_str      = json_schema_to_grammar(schema);
+                    SRV_DBG("Converted grammar: %s\n", grammar_str.c_str());
+                    params.sampling.grammar      = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, std::move(grammar_str)};
+                } catch (const std::exception & e) {
+                    throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
+                }
+            } else {
+                std::string grammar_str = json_value(data, "grammar", std::string());
+                if (!grammar_str.empty()) {
+                    // grammar_type key is set by the server when converting chat template grammars
+                    std::string grammar_type = json_value(data, "grammar_type", std::string());
+                    if (grammar_type == "tool_calls") {
+                        params.sampling.grammar = {COMMON_GRAMMAR_TYPE_TOOL_CALLS, std::move(grammar_str)};
+                    } else {
+                        // explicit grammar from the user (API field "grammar")
+                        params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, std::move(grammar_str)};
+                    }
+                    SRV_DBG("Grammar (%s): %s\n", grammar_type.c_str(), common_grammar_value(params.sampling.grammar).c_str());
+                }
+            }
+        }));
+
+    add((new field_bool("grammar_lazy", params.sampling.grammar_lazy))
+        ->set_desc("Whether to apply grammar constraints lazily, only when triggered (instead of at every step)"));
+
+    //
+    // Chat parser params
+    //
+
+    // TODO: change this to string field instead
+    add((new field_json("chat_format"))
+        ->set_desc("Chat format used internally by the server")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.chat_parser_params.format = static_cast<common_chat_format>(data.at("chat_format").get<int>());
+            SRV_INF("Chat format: %s\n", common_chat_format_name(ctx.params.chat_parser_params.format));
+        }));
+
+    add((new field_str("reasoning_format"))
+        ->set_desc("Reasoning format for chain-of-thought models")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            auto reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
+            ctx.params.chat_parser_params.reasoning_format = reasoning_format;
+            ctx.params.chat_parser_params.reasoning_in_content = ctx.params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+        }));
+
+    add((new field_str("generation_prompt"))
+        ->set_desc("Generation prompt appended to the chat template output")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            std::string s = data.at("generation_prompt").get<std::string>();
+            ctx.params.chat_parser_params.generation_prompt = s;
+            ctx.params.sampling.generation_prompt = s;
+        }));
+
+    add((new field_bool("parse_tool_calls", params.chat_parser_params.parse_tool_calls))
+        ->set_desc("Whether to parse tool calls from the generated output"));
+
+    add((new field_str("chat_parser"))
+        ->set_desc("Chat parser configuration string")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.chat_parser_params.parser.load(data.at("chat_parser").get<std::string>());
+        }));
+
+    add((new field_json("continue_final_message"))
+        ->set_desc("Whether to continue the final message of the chat template")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            auto continuation = common_chat_continuation_parse(data.at("continue_final_message"));
+            ctx.params.chat_parser_params.is_continuation = continuation != COMMON_CHAT_CONTINUATION_NONE;
+        }));
+
+    add((new field_bool("echo", params.chat_parser_params.echo))
+        ->set_desc("Whether to echo the input tokens in the output"));
+
+    //
+    // Token-level fields (require vocab)
+    //
+
+    add((new field_json("preserved_tokens"))
+        ->set_desc("List of token strings that must not be split during tokenization")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            for (const auto & t : data.at("preserved_tokens")) {
+                auto ids = common_tokenize(ctx.vocab, t.get<std::string>(), false, true);
+                if (ids.size() == 1) {
+                    ctx.params.sampling.preserved_tokens.insert(ids[0]);
+                }
+            }
+        }));
+
+    add((new field_json("grammar_triggers"))
+        ->set_desc("List of strings or patterns that trigger grammar-constrained generation")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            for (const auto & t : data.at("grammar_triggers")) {
+                server_grammar_trigger ct(t);
+                if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
+                    const auto & word = ct.value.value;
+                    auto ids = common_tokenize(ctx.vocab, word, false, true);
+                    if (ids.size() == 1) {
+                        auto token = ids[0];
+                        if (std::find(ctx.params.sampling.preserved_tokens.begin(), ctx.params.sampling.preserved_tokens.end(), (llama_token) token) == ctx.params.sampling.preserved_tokens.end()) {
+                            throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
+                        }
+                        common_grammar_trigger trigger;
+                        trigger.type  = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
+                        trigger.value = word;
+                        trigger.token = token;
+                        ctx.params.sampling.grammar_triggers.push_back(std::move(trigger));
+                    } else {
+                        ctx.params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
+                    }
+                } else {
+                    ctx.params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
+                }
+            }
+            if (ctx.params.sampling.grammar_lazy && ctx.params.sampling.grammar_triggers.empty()) {
+                throw std::runtime_error("Error: no triggers set for lazy grammar!");
+            }
+        }));
+
+    add((new field_bool("reasoning_control", params.sampling.reasoning_control))
+        ->set_desc("Create the budget sampler on demand so reasoning can be ended at runtime"));
+
+    add((new field_num("reasoning_budget_tokens", params.sampling.reasoning_budget_tokens))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("Number of tokens in the reasoning budget (-1 = disabled)"));
+
+    add((new field_str("reasoning_budget_start_tag"))
+        ->set_desc("Token string marking the start of the reasoning budget section")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            ctx.params.sampling.reasoning_budget_start = common_tokenize(ctx.vocab, data.at("reasoning_budget_start_tag").get<std::string>(), false, true);
+        }));
+
+    add((new field_str("reasoning_budget_end_tag"))
+        ->set_desc("Token string marking the end of the reasoning budget section")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            std::string end_tag = data.at("reasoning_budget_end_tag").get<std::string>();
+            ctx.params.sampling.reasoning_budget_end = common_tokenize(ctx.vocab, end_tag, false, true);
+        }));
+
+    add((new field_str("reasoning_budget_message"))
+        ->set_desc("Message to prepend to the reasoning budget end tag when forcing it")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            std::string end_tag = json_value(data, "reasoning_budget_end_tag", std::string());
+            std::string message = data.at("reasoning_budget_message").get<std::string>();
+            ctx.params.sampling.reasoning_budget_forced = common_tokenize(ctx.vocab, message + end_tag, false, true);
+        }));
+
+    add((new field_json("logit_bias"))
+        ->set_desc("Modify the likelihood of specific tokens. Accepts an array of [token, bias] pairs or an object mapping token to bias. Use false as bias to ban a token")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            ctx.params.sampling.logit_bias.clear();
+            const auto & logit_bias = data.at("logit_bias");
+            const int n_vocab = llama_vocab_n_tokens(ctx.vocab);
+            auto parse_bias = [](const json & v, float & bias) -> bool {
+                if (v.is_number())                        { bias = v.get<float>(); return true; }
+                if (v.is_boolean() && !v.get<bool>())     { bias = -INFINITY;      return true; }
+                return false;
+            };
+            if (logit_bias.is_array()) {
+                for (const auto & el : logit_bias) {
+                    if (!el.is_array() || el.size() != 2) continue;
+                    float bias;
+                    if (!parse_bias(el[1], bias)) continue;
+                    if (el[0].is_number_integer()) {
+                        llama_token tok = el[0].get<llama_token>();
+                        if (tok >= 0 && tok < n_vocab) ctx.params.sampling.logit_bias.push_back({tok, bias});
+                    } else if (el[0].is_string()) {
+                        for (auto tok : common_tokenize(ctx.vocab, el[0].get<std::string>(), false))
+                            ctx.params.sampling.logit_bias.push_back({tok, bias});
+                    }
+                }
+            } else if (logit_bias.is_object()) {
+                for (const auto & el : logit_bias.items()) {
+                    float bias;
+                    if (!parse_bias(el.value(), bias)) continue;
+                    char * end;
+                    llama_token tok = strtol(el.key().c_str(), &end, 10);
+                    if (*end == 0) {
+                        if (tok >= 0 && tok < n_vocab) ctx.params.sampling.logit_bias.push_back({tok, bias});
+                    } else {
+                        for (auto t : common_tokenize(ctx.vocab, el.key(), false))
+                            ctx.params.sampling.logit_bias.push_back({t, bias});
+                    }
+                }
+            }
+        }));
+
+    add((new field_bool("ignore_eos", params.sampling.ignore_eos))
+        ->set_desc("Ignore the end-of-sequence token and continue generating")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.logit_bias_eog != nullptr);
+            ctx.params.sampling.ignore_eos = data.at("ignore_eos").get<bool>();
+            if (ctx.params.sampling.ignore_eos && ctx.logit_bias_eog) {
+                ctx.params.sampling.logit_bias.insert(
+                    ctx.params.sampling.logit_bias.end(),
+                    ctx.logit_bias_eog->begin(), ctx.logit_bias_eog->end());
+            }
+        }));
+
+    add((new field_json("stop"))
+        ->set_desc("Specify stopping strings. Generation stops when one is produced, and the string is not included in the output")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.antiprompt.clear();
+            const auto & stop = data.at("stop");
+            if (stop.is_array()) {
+                for (const auto & word : stop) {
+                    if (!word.empty()) ctx.params.antiprompt.push_back(word);
+                }
+            } else if (stop.is_string()) {
+                ctx.params.antiprompt.push_back(stop.get<std::string>());
+            }
+            // fall back to CLI defaults if the request provided no effective stop strings
+            if (ctx.params.antiprompt.empty()) {
+                ctx.params.antiprompt = params_base.antiprompt;
+            }
+        }));
+
+    add((new field_json("samplers"))
+        ->set_desc("The order in which samplers are applied. An array of sampler type names, or a single string of sampler chars")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            const auto & samplers = data.at("samplers");
+            if (samplers.is_array()) {
+                ctx.params.sampling.samplers = common_sampler_types_from_names(samplers);
+            } else if (samplers.is_string()) {
+                ctx.params.sampling.samplers = common_sampler_types_from_chars(samplers.get<std::string>());
+            }
+        }));
+
+    return fields;
+}
+
+task_params eval_llama_cmpl_schema(
+                const llama_vocab * vocab,
+                const common_params & params_base,
+                const int n_ctx_slot,
+                const std::vector<llama_logit_bias> & logit_bias_eog,
+                const json & data) {
+    task_params params;
+
+    // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
+    params.sampling      = params_base.sampling;
+    params.speculative   = params_base.speculative;
+    params.n_keep        = params_base.n_keep;
+    params.n_predict     = params_base.n_predict;
+    params.n_cache_reuse = params_base.n_cache_reuse;
+    params.cache_prompt  = params_base.cache_prompt;
+    params.antiprompt    = params_base.antiprompt;
+
+    // enabling this will output extra debug information in the HTTP responses from the server
+    params.verbose       = params_base.verbosity > 9;
+
+    params.chat_parser_params.reasoning_format = params_base.reasoning_format;
+
+    // create context and schema
+    field_eval_context ctx(params);
+    ctx.vocab          = vocab;
+    ctx.logit_bias_eog = &logit_bias_eog;
+
+    auto schema = make_llama_cmpl_schema(params_base, params);
+
+    // eval all fields in the schema
+    for (const auto & f : schema) {
+        f->eval(ctx, data);
+    }
+
+    // post-processing
+    {
+        if (params.sampling.penalty_last_n == -1) {
+            // note: should be the slot's context and not the full context, but it's ok
+            params.sampling.penalty_last_n = n_ctx_slot;
+        }
+
+        if (params.sampling.dry_penalty_last_n == -1) {
+            params.sampling.dry_penalty_last_n = n_ctx_slot;
+        }
+
+        // if "reasoning_format" is not provided, its handler will not be called, we will need to handle it here
+        auto reasoning_format = params.chat_parser_params.reasoning_format;
+        params.chat_parser_params.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+    }
+
+    // debugging
+    {
+        auto budget = params.sampling.reasoning_budget_tokens;
+        SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n",
+                budget, params.sampling.generation_prompt.c_str(),
+                params.sampling.reasoning_budget_start.size(),
+                params.sampling.reasoning_budget_end.size(),
+                params.sampling.reasoning_budget_forced.size());
+    }
+
+    return params;
+}
+
+//
+// eval() implementations
+//
+
+static void handle_with_catch(const char * name, std::function<void()> func) {
+    try {
+        func();
+    } catch (const std::exception & e) {
+        throw std::invalid_argument(string_format("Field '%s': %s", name, e.what()));
+    }
+}
+
+template <typename T>
+void field_num<T>::eval(field_eval_context & ctx, const json & data) {
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                if (custom_handler) {
+                custom_handler(ctx, data);
+                } else if (!is_hard_limit) {
+                    val = std::max(min, std::min(max, data.at(n).template get<T>()));
+                } else {
+                    T tmp = data.at(n).template get<T>();
+                    if (tmp < min || tmp > max) {
+                        throw std::invalid_argument(std::string("Value must be between ") + std::to_string(min) + " <= value <= " + std::to_string(max) + ", but got " + std::to_string(tmp));
+                    }
+                    val = tmp;
+                }
+            });
+            return;
+        }
+    }
+}
+
+void field_str::eval(field_eval_context & ctx, const json & data) {
+    GGML_ASSERT(custom_handler);
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                custom_handler(ctx, data);
+            });
+            return;
+        }
+    }
+}
+
+void field_bool::eval(field_eval_context & ctx, const json & data) {
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                if (custom_handler) {
+                    custom_handler(ctx, data);
+                } else {
+                    val = data.at(n).get<bool>();
+                }
+            });
+            return;
+        }
+    }
+}
+
+void field_json::eval(field_eval_context & ctx, const json & data) {
+    GGML_ASSERT(custom_handler);
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                custom_handler(ctx, data);
+            });
+            return;
+        }
+    }
+}
+
+void field_nested::eval(field_eval_context & ctx, const json & data) {
+    for (const auto & n : name) {
+        if (data.contains(n) && data.at(n).is_object()) {
+            for (auto & f : subfields) {
+                f->eval(ctx, data.at(n));
+            }
+            return;
+        }
+    }
+}
+
+} // namespace server_schema
diff --git a/tools/server/server-schema.h b/tools/server/server-schema.h
new file mode 100644
index 0000000000..08cf427dc9
--- /dev/null
+++ b/tools/server/server-schema.h
@@ -0,0 +1,105 @@
+#pragma once
+
+#include "server-common.h"
+#include "server-task.h"
+
+#include "sampling.h"
+#include "speculative.h"
+
+#include <climits>
+#include <functional>
+#include <limits>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace server_schema {
+
+struct field_eval_context {
+    task_params & params;
+    const llama_vocab * vocab = nullptr;
+    const std::vector<llama_logit_bias> * logit_bias_eog = nullptr;
+    field_eval_context(task_params & params) : params(params) {}
+};
+
+using field_handler = std::function<void(field_eval_context &, const json &)>;
+
+struct field {
+    std::vector<const char *> name;
+    const char * desc = "";
+    field_handler custom_handler;
+    field() = default;
+    field(const char * n) : name({n}) {}
+    virtual ~field() = default;
+    field * set_desc(const char * s) {
+        desc = s;
+        return this;
+    }
+    // if 'name' is present, use it, otherwise look for aliases following the order they were added
+    field * add_alias(const char * n) {
+        name.push_back(n);
+        return this;
+    }
+    field * set_handler(field_handler h) { this->custom_handler = h; return this; }
+    virtual void eval(field_eval_context & ctx, const json & data) = 0;
+};
+
+template <typename T = int32_t>
+struct field_num : public field {
+    T & val;
+    T min = std::numeric_limits<T>::lowest();
+    T max = std::numeric_limits<T>::max();
+    bool is_hard_limit = false; // if true, throw error if the value is invalid
+    field_num(const char * n, T & val) : field(n), val(val) {}
+    // limits are inclusive, min <= value <= max
+    field_num * set_limits(T min, T max) {
+        this->min = min;
+        this->max = max;
+        return this;
+    }
+    field_num * set_hard_limits(T min, T max) {
+        set_limits(min, max);
+        is_hard_limit = true;
+        return this;
+    }
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_str : public field {
+    field_str(const char * n) : field(n) {}
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_bool : public field {
+    bool & val;
+    field_bool(const char * n, bool & val) : field(n), val(val) {}
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_json : public field {
+    field_json(const char * n) : field(n) {}
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_nested : public field {
+    std::vector<std::unique_ptr<field>> subfields;
+    field_nested(const char * n) : field(n) {}
+    field_nested * add_subfield(field * f) {
+        subfields.emplace_back(std::unique_ptr<field>(f));
+        return this;
+    }
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(
+                    const common_params & params_base,
+                    task_params & params);
+
+task_params eval_llama_cmpl_schema(
+                    const llama_vocab * vocab,
+                    const common_params & params_base,
+                    const int n_ctx_slot,
+                    const std::vector<llama_logit_bias> & logit_bias_eog,
+                    const json & data);
+
+} // namespace server_schema
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 72a4bd076a..9ba039c8b8 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -232,396 +232,8 @@ common_chat_msg task_result_state::update_chat_msg(
     return chat_msg;
 }
 
-//
-// server_task
 //
 
-task_params server_task::params_from_json_cmpl(
-        const llama_vocab * vocab,
-        const common_params & params_base,
-        const int n_ctx_slot,
-        const std::vector<llama_logit_bias> & logit_bias_eog,
-        const json & data) {
-    task_params params;
-
-    // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
-    task_params defaults;
-    defaults.sampling      = params_base.sampling;
-    defaults.speculative   = params_base.speculative;
-    defaults.n_keep        = params_base.n_keep;
-    defaults.n_predict     = params_base.n_predict;
-    defaults.n_cache_reuse = params_base.n_cache_reuse;
-    defaults.cache_prompt  = params_base.cache_prompt;
-    defaults.antiprompt    = params_base.antiprompt;
-
-    // enabling this will output extra debug information in the HTTP responses from the server
-    params.verbose           = params_base.verbosity > 9;
-    params.timings_per_token = json_value(data, "timings_per_token", false);
-
-    params.stream           = json_value(data,       "stream",             false);
-    auto stream_opt         = json_value(data,       "stream_options",     json::object());
-    params.include_usage    = json_value(stream_opt, "include_usage",      false);
-    params.cache_prompt     = json_value(data,       "cache_prompt",       defaults.cache_prompt);
-    params.return_tokens    = json_value(data,       "return_tokens",      false);
-    params.return_progress  = json_value(data,       "return_progress",    false);
-    auto max_tokens         = json_value(data,       "max_tokens",         defaults.n_predict);
-    params.n_predict        = json_value(data,       "n_predict",          json_value(data, "max_completion_tokens", max_tokens));
-    params.n_indent         = json_value(data,       "n_indent",           defaults.n_indent);
-    params.n_keep           = json_value(data,       "n_keep",             defaults.n_keep);
-    params.n_discard        = json_value(data,       "n_discard",          defaults.n_discard);
-    params.n_discard        = std::max(0, params.n_discard);
-    params.n_cmpl           = json_value(data,       "n_cmpl",             json_value(data, "n", 1));
-    params.n_cache_reuse    = json_value(data,       "n_cache_reuse",      defaults.n_cache_reuse);
-    //params.t_max_prompt_ms  = json_value(data,       "t_max_prompt_ms",    defaults.t_max_prompt_ms); // TODO: implement
-    params.t_max_predict_ms = json_value(data,       "t_max_predict_ms",   defaults.t_max_predict_ms);
-    params.response_fields  = json_value(data,       "response_fields",    std::vector<std::string>());
-
-    params.sampling.top_k              = json_value(data, "top_k",               defaults.sampling.top_k);
-    params.sampling.top_p              = json_value(data, "top_p",               defaults.sampling.top_p);
-    params.sampling.min_p              = json_value(data, "min_p",               defaults.sampling.min_p);
-    params.sampling.top_n_sigma        = json_value(data, "top_n_sigma",         defaults.sampling.top_n_sigma);
-    params.sampling.xtc_probability    = json_value(data, "xtc_probability",     defaults.sampling.xtc_probability);
-    params.sampling.xtc_threshold      = json_value(data, "xtc_threshold",       defaults.sampling.xtc_threshold);
-    params.sampling.typ_p              = json_value(data, "typical_p",           defaults.sampling.typ_p);
-    params.sampling.temp               = json_value(data, "temperature",         defaults.sampling.temp);
-    params.sampling.dynatemp_range     = json_value(data, "dynatemp_range",      defaults.sampling.dynatemp_range);
-    params.sampling.dynatemp_exponent  = json_value(data, "dynatemp_exponent",   defaults.sampling.dynatemp_exponent);
-    params.sampling.penalty_last_n     = json_value(data, "repeat_last_n",       defaults.sampling.penalty_last_n);
-    params.sampling.penalty_repeat     = json_value(data, "repeat_penalty",      defaults.sampling.penalty_repeat);
-    params.sampling.penalty_freq       = json_value(data, "frequency_penalty",   defaults.sampling.penalty_freq);
-    params.sampling.penalty_present    = json_value(data, "presence_penalty",    defaults.sampling.penalty_present);
-    params.sampling.dry_multiplier     = json_value(data, "dry_multiplier",      defaults.sampling.dry_multiplier);
-    params.sampling.dry_base           = json_value(data, "dry_base",            defaults.sampling.dry_base);
-    params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length",  defaults.sampling.dry_allowed_length);
-    params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n",  defaults.sampling.dry_penalty_last_n);
-    params.sampling.mirostat           = json_value(data, "mirostat",            defaults.sampling.mirostat);
-    params.sampling.mirostat_tau       = json_value(data, "mirostat_tau",        defaults.sampling.mirostat_tau);
-    params.sampling.mirostat_eta       = json_value(data, "mirostat_eta",        defaults.sampling.mirostat_eta);
-    params.sampling.adaptive_target    = json_value(data, "adaptive_target",     defaults.sampling.adaptive_target);
-    params.sampling.adaptive_decay     = json_value(data, "adaptive_decay",      defaults.sampling.adaptive_decay);
-    params.sampling.seed               = json_value(data, "seed",                defaults.sampling.seed);
-    params.sampling.n_probs            = json_value(data, "n_probs",             defaults.sampling.n_probs);
-    params.sampling.min_keep           = json_value(data, "min_keep",            defaults.sampling.min_keep);
-    params.sampling.backend_sampling   = json_value(data, "backend_sampling",    defaults.sampling.backend_sampling);
-    params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
-
-    params.speculative = defaults.speculative;
-
-    // TODO: to keep things simple, we disable speculative parameter adjustments for now
-#if 0
-    // TODO: for now, be able to adjust only the draft-model based speculative parameters
-    params.speculative.draft.n_min = json_value(data, "speculative.n_min", defaults.speculative.draft.n_min);
-    params.speculative.draft.n_max = json_value(data, "speculative.n_max", defaults.speculative.draft.n_max);
-    params.speculative.draft.p_min = json_value(data, "speculative.p_min", defaults.speculative.draft.p_min);
-
-    params.speculative.draft.n_min = std::min(params.speculative.draft.n_max, params.speculative.draft.n_min);
-    params.speculative.draft.n_min = std::max(params.speculative.draft.n_min, 0);
-    params.speculative.draft.n_max = std::max(params.speculative.draft.n_max, 0);
-
-    // for debugging and research purposes
-    params.speculative.type = common_speculative_type_from_name(json_value(data, "speculative.type", common_speculative_type_to_str(defaults.speculative.type)));
-
-    params.speculative.ngram_size_n     = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n);
-    params.speculative.ngram_size_m     = json_value(data, "speculative.ngram_size_m", defaults.speculative.ngram_size_m);
-    params.speculative.ngram_min_hits   = json_value(data, "speculative.ngram_m_hits", defaults.speculative.ngram_min_hits);
-
-    params.speculative.ngram_size_n     = std::max(std::min(1, (int) params.speculative.ngram_size_n),     1024);
-    params.speculative.ngram_size_m     = std::max(std::min(1, (int) params.speculative.ngram_size_m),     1024);
-    params.speculative.ngram_min_hits   = std::max(std::min(1, (int) params.speculative.ngram_min_hits),   1024);
-#endif
-
-    // Use OpenAI API logprobs only if n_probs wasn't provided
-    if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
-        params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs);
-    }
-
-    if (data.contains("lora")) {
-        if (data.at("lora").is_array()) {
-            params.lora = parse_lora_request(data.at("lora"));
-        } else {
-            throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
-        }
-    } else {
-        params.lora = {};
-    }
-
-    // TODO: add more sanity checks for the input parameters
-
-    if (params.sampling.penalty_last_n < -1) {
-        throw std::runtime_error("Error: repeat_last_n must be >= -1");
-    }
-
-    if (params.sampling.dry_penalty_last_n < -1) {
-        throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        // note: should be the slot's context and not the full context, but it's ok
-        params.sampling.penalty_last_n = n_ctx_slot;
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        params.sampling.dry_penalty_last_n = n_ctx_slot;
-    }
-
-    if (params.sampling.dry_base < 1.0f) {
-        params.sampling.dry_base = defaults.sampling.dry_base;
-    }
-
-    // sequence breakers for DRY
-    {
-        // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
-        // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
-
-        if (data.contains("dry_sequence_breakers")) {
-            params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
-            if (params.sampling.dry_sequence_breakers.empty()) {
-                throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
-            }
-        }
-    }
-
-    // process "json_schema" and "grammar"
-    if (data.contains("json_schema") && !data.contains("grammar")) {
-        try {
-            auto schema                  = json_value(data, "json_schema", json::object());
-            SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
-            std::string grammar_str      = json_schema_to_grammar(schema);
-            SRV_DBG("Converted grammar: %s\n", grammar_str.c_str());
-            params.sampling.grammar      = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, std::move(grammar_str)};
-        } catch (const std::exception & e) {
-            throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
-        }
-    } else {
-        params.sampling.grammar = defaults.sampling.grammar;
-
-        std::string grammar_str = json_value(data, "grammar", std::string());
-        if (!grammar_str.empty()) {
-            // grammar_type key is set by the server when converting chat template grammars
-            std::string grammar_type = json_value(data, "grammar_type", std::string());
-            if (grammar_type == "tool_calls") {
-                params.sampling.grammar = {COMMON_GRAMMAR_TYPE_TOOL_CALLS, std::move(grammar_str)};
-            } else {
-                // explicit grammar from the user (API field "grammar")
-                params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, std::move(grammar_str)};
-            }
-            SRV_DBG("Grammar (%s): %s\n", grammar_type.c_str(), common_grammar_value(params.sampling.grammar).c_str());
-        }
-        params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
-        SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
-    }
-
-    {
-        auto it = data.find("chat_format");
-        if (it != data.end()) {
-            params.chat_parser_params.format = static_cast<common_chat_format>(it->get<int>());
-            SRV_INF("Chat format: %s\n", common_chat_format_name(params.chat_parser_params.format));
-        } else {
-            params.chat_parser_params.format = defaults.chat_parser_params.format;
-        }
-        common_reasoning_format reasoning_format = params_base.reasoning_format;
-        if (data.contains("reasoning_format")) {
-            reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
-        }
-        params.chat_parser_params.reasoning_format = reasoning_format;
-        params.chat_parser_params.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
-        params.chat_parser_params.generation_prompt = json_value(data, "generation_prompt", std::string());
-        params.sampling.generation_prompt = params.chat_parser_params.generation_prompt;
-        SRV_DBG("Generation prompt: '%s'\n", params.chat_parser_params.generation_prompt.c_str());
-        params.chat_parser_params.parse_tool_calls = json_value(data, "parse_tool_calls", false);
-        if (data.contains("chat_parser")) {
-            params.chat_parser_params.parser.load(data.at("chat_parser").get<std::string>());
-        }
-        if (data.contains("continue_final_message")) {
-            auto continuation = common_chat_continuation_parse(data.at("continue_final_message"));
-            params.chat_parser_params.is_continuation = continuation != COMMON_CHAT_CONTINUATION_NONE;
-        }
-        params.chat_parser_params.echo = json_value(data, "echo", false);
-    }
-
-    {
-        const auto preserved_tokens = data.find("preserved_tokens");
-        if (preserved_tokens != data.end()) {
-            for (const auto & t : *preserved_tokens) {
-                auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
-                if (ids.size() == 1) {
-                    SRV_DBG("Preserved token: %d\n", ids[0]);
-                    params.sampling.preserved_tokens.insert(ids[0]);
-                } else {
-                    // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
-                    SRV_DBG("Not preserved because more than 1 token: %s\n", t.get<std::string>().c_str());
-                }
-            }
-        }
-        const auto grammar_triggers = data.find("grammar_triggers");
-        if (grammar_triggers != data.end()) {
-            for (const auto & t : *grammar_triggers) {
-                server_grammar_trigger ct(t);
-                if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
-                    const auto & word = ct.value.value;
-                    auto ids = common_tokenize(vocab, word, /* add_special= */ false, /* parse_special= */ true);
-                    if (ids.size() == 1) {
-                        auto token = ids[0];
-                        if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) {
-                            throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
-                        }
-                        SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
-                        common_grammar_trigger trigger;
-                        trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
-                        trigger.value = word;
-                        trigger.token = token;
-                        params.sampling.grammar_triggers.push_back(std::move(trigger));
-                    } else {
-                        SRV_DBG("Grammar trigger word: `%s`\n", word.c_str());
-                        params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
-                    }
-                } else {
-                    if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) {
-                        SRV_DBG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str());
-                    } else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) {
-                        SRV_DBG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str());
-                    } else {
-                        throw std::runtime_error("Unknown grammar trigger type");
-                    }
-                    params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
-                }
-            }
-        }
-        if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) {
-            throw std::runtime_error("Error: no triggers set for lazy grammar!");
-        }
-    }
-
-    // Parse reasoning budget sampler parameters
-    {
-        const int32_t budget = json_value(data, "reasoning_budget_tokens", (int32_t) -1);
-        const auto start_tag = json_value(data, "reasoning_budget_start_tag", std::string());
-        const auto end_tag   = json_value(data, "reasoning_budget_end_tag", std::string());
-        const auto message   = json_value(data, "reasoning_budget_message", std::string());
-        params.sampling.reasoning_budget_tokens = budget;
-        params.sampling.reasoning_control = json_value(data, "reasoning_control", false);
-
-        if (!start_tag.empty()) {
-            params.sampling.reasoning_budget_start = common_tokenize(vocab, start_tag, false, true);
-        }
-        if (!end_tag.empty()) {
-            params.sampling.reasoning_budget_end = common_tokenize(vocab, end_tag, false, true);
-            params.sampling.reasoning_budget_forced = common_tokenize(vocab, message + end_tag, false, true);
-
-            SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n",
-                budget, params.sampling.generation_prompt.c_str(),
-                params.sampling.reasoning_budget_start.size(),
-                params.sampling.reasoning_budget_end.size(),
-                params.sampling.reasoning_budget_forced.size());
-        }
-    }
-
-    {
-        params.sampling.logit_bias.clear();
-
-        const auto & logit_bias = data.find("logit_bias");
-        if (logit_bias != data.end() && logit_bias->is_array()) {
-            const int n_vocab = llama_vocab_n_tokens(vocab);
-            for (const auto & el : *logit_bias) {
-                // TODO: we may want to throw errors here, in case "el" is incorrect
-                if (el.is_array() && el.size() == 2) {
-                    float bias;
-                    if (el[1].is_number()) {
-                        bias = el[1].get<float>();
-                    } else if (el[1].is_boolean() && !el[1].get<bool>()) {
-                        bias = -INFINITY;
-                    } else {
-                        continue;
-                    }
-
-                    if (el[0].is_number_integer()) {
-                        llama_token tok = el[0].get<llama_token>();
-                        if (tok >= 0 && tok < n_vocab) {
-                            params.sampling.logit_bias.push_back({tok, bias});
-                        }
-                    } else if (el[0].is_string()) {
-                        auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
-                        for (auto tok : toks) {
-                            params.sampling.logit_bias.push_back({tok, bias});
-                        }
-                    }
-                }
-            }
-        } else if (logit_bias != data.end() && logit_bias->is_object()) {
-            const int n_vocab = llama_vocab_n_tokens(vocab);
-            for (const auto & el : logit_bias->items()) {
-                float bias;
-                const auto & key = el.key();
-                const auto & value = el.value();
-                if (value.is_number()) {
-                    bias = value.get<float>();
-                } else if (value.is_boolean() && !value.get<bool>()) {
-                    bias = -INFINITY;
-                } else {
-                    continue;
-                }
-
-                char *end;
-                llama_token tok = strtol(key.c_str(), &end, 10);
-                if (*end == 0) {
-                    if (tok >= 0 && tok < n_vocab) {
-                        params.sampling.logit_bias.push_back({tok, bias});
-                    }
-                } else {
-                    auto toks = common_tokenize(vocab, key, false);
-                    for (auto tok : toks) {
-                        params.sampling.logit_bias.push_back({tok, bias});
-                    }
-                }
-            }
-        }
-
-        params.sampling.ignore_eos = json_value(data, "ignore_eos", params_base.sampling.ignore_eos);
-        if (params.sampling.ignore_eos) {
-            params.sampling.logit_bias.insert(
-                    params.sampling.logit_bias.end(),
-                    logit_bias_eog.begin(), logit_bias_eog.end());
-        }
-    }
-
-    {
-        params.antiprompt.clear();
-
-        const auto & stop = data.find("stop");
-        if (stop != data.end() && stop->is_array()) {
-            for (const auto & word : *stop) {
-                if (!word.empty()) {
-                    params.antiprompt.push_back(word);
-                }
-            }
-        }
-        // set reverse prompt from cli args if not set in the request
-        if (params.antiprompt.empty()) {
-            params.antiprompt = defaults.antiprompt;
-        }
-    }
-
-    {
-        const auto samplers = data.find("samplers");
-        if (samplers != data.end()) {
-            if (samplers->is_array()) {
-                params.sampling.samplers = common_sampler_types_from_names(*samplers);
-            } else if (samplers->is_string()){
-                params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
-            }
-        } else {
-            params.sampling.samplers = defaults.sampling.samplers;
-        }
-    }
-
-    if (params.n_cmpl > params_base.n_parallel) {
-        throw std::runtime_error("n_cmpl cannot be greater than the number of slots, please increase -np");
-    }
-
-    return params;
-}
-
-//
 // result_timings
 //
 
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 1a03d5f266..299c279d7d 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -210,13 +210,6 @@ struct server_task {
         }
     }
 
-    static task_params params_from_json_cmpl(
-        const llama_vocab * vocab,
-        const common_params & params_base,
-        const int n_ctx_slot,
-        const std::vector<llama_logit_bias> & logit_bias_eog,
-        const json & data);
-
     // utility function
     static std::unordered_set<int> get_list_id(const std::vector<server_task> & tasks) {
         std::unordered_set<int> ids(tasks.size());