mcp-tournament

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Run a full tournament

tournament_evaluate

Runs 1-4 AI models across benchmark scenarios, uses judge panels to score responses, and saves a ranked leaderboard. Requires prior planning and user confirmation; paid model calls.

Instructions

Costs money. Makes paid model calls and writes results to disk. Call tournament_plan_run first and get the user's yes in chat. Clients that support forms will also get a confirm form. Runs 1-4 models across a bench's scenarios and saves a leaderboard. Can take several minutes. judgePanel picks judge models and personas per seat and overrides judges and judgeModels. turns sets one turn count for every scenario. participantModel picks the simulated user model. synthesizerModel reconciles judge scores. OpenRouter is the default; anthropic: refs bill the user's Anthropic API key. Use tournament_quick_test for a cheap check.

Input Schema

Name	Required	Description	Default
`turns`	No	1-10 turns for every scenario. Omit to use each scenario's own default.
`judges`	No	Number of judges on the scoring panel, 1-5. Defaults to 3. Ignored when `judgeModels` is provided.
`models`	Yes	1-4 model refs to compare. Bare IDs use OpenRouter. anthropic: refs use your Anthropic API key. Add @level to set how hard a model thinks, e.g. "openai/gpt-6.1-sol@high". Levels: none, minimal, low, medium, high, xhigh, max; each model accepts only some (see tournament_options). Leave it off for the model's default. The same model may appear twice at different levels.
`plugin`	No	Bench to run, e.g. "dnd" or "coding". Defaults to "dnd".	dnd
`scenarios`	No	Scenario IDs to run. Omit to run every scenario in the bench.
`judgePanel`	No	1-5 judge seats. Each may set model and either persona or customPersona with a lens (1-1000 characters) and optional name (1-60 characters). Add @level to set how hard a model thinks, e.g. "openai/gpt-6.1-sol@high". Levels: none, minimal, low, medium, high, xhigh, max; each model accepts only some (see tournament_options). Leave it off for the model's default. The same model may appear twice at different levels.
`judgeModels`	No	1-5 OpenRouter model IDs, one per judge seat, filled in this fixed order: 1. Rules Judge (accuracy, tool_usage); 2. Creative Judge (clarity, creativity, communication); 3. Holistic Judge (overall_quality, task_completion); 4. Authentic Voice Judge (authentic_voice); 5. Context Judge (context, consistency). When provided, the panel size equals the length of this list and overrides `judges`. Omit to use the default model for each seat. Add @level to set how hard a model thinks, e.g. "openai/gpt-6.1-sol@high". Levels: none, minimal, low, medium, high, xhigh, max; each model accepts only some (see tournament_options). Leave it off for the model's default. The same model may appear twice at different levels.
`participantModel`	No	Model ref for the simulated user who sends follow-up messages. Omit for the default. Add @level to set how hard a model thinks, e.g. "openai/gpt-6.1-sol@high". Levels: none, minimal, low, medium, high, xhigh, max; each model accepts only some (see tournament_options). Leave it off for the model's default. The same model may appear twice at different levels.
`synthesizerModel`	No	OpenRouter model ID that reconciles the judges' scores into one final score. Omit to use the default synthesizer model. Add @level to set how hard a model thinks, e.g. "openai/gpt-6.1-sol@high". Levels: none, minimal, low, medium, high, xhigh, max; each model accepts only some (see tournament_options). Leave it off for the model's default. The same model may appear twice at different levels.

Output Schema

Name	Required	Description	Default
`runId`	Yes
`judges`	Yes
`plugin`	Yes
`status`	Yes
`entries`	Yes
`message`	No
`failures`	Yes
`resultsDir`	Yes
`judgeFailures`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 17, 2026

TDQS

A4.8/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only declare the generic safety profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false). The description adds the details that actually change agent behavior: it costs money, makes paid model calls, writes results to disk, may take several minutes, and triggers a confirm form on clients that support forms. That is meaningful disclosure beyond structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the highest-stakes facts (cost, paid calls, disk writes, confirmation) before the mechanics. It is dense and the back half is a run-on sequence of parameter roles, but nearly every clause carries distinct information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained. Given a 9-parameter, non-idempotent, costly tool, the description covers cost, duration, disk side effects, prerequisites, confirmation flow, and the cheaper alternative — enough for an agent to decide and call safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds precedence semantics the schema does not state: judgePanel 'overrides judges and judgeModels', turns applies one count to every scenario, and synthesizerModel reconciles judge scores. It does not explain every parameter, but it clarifies the interaction between overlapping ones.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action and scope: runs 1-4 models across a bench's scenarios and saves a leaderboard. It also distinguishes itself from siblings by naming tournament_quick_test and tournament_plan_run, so an agent can route correctly without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit preconditions (call tournament_plan_run first, get the user's yes in chat) and an explicit alternative for the cheap path (tournament_quick_test). This is exactly the when/when-not/alternative structure the dimension rewards.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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