mcp-tournament

Preview a tournament run

tournament_plan_run

Read-onlyIdempotent

Preview a tournament plan: check candidates, fill defaults, see cost and provider setup needed, all without model calls. Confirm before evaluation.

Instructions

Free preview. Checks your choices against the model catalog, fills in defaults, and shows the plan, rough cost, and any provider setup still needed. Makes no model calls and creates no run folder. Show the preview and get the user's yes before tournament_evaluate.

Input Schema

Name	Required	Description	Default
`bench`	No	Bench name. Defaults to "dnd". Call tournament_options for choices.	dnd
`turns`	No	1-10 turns for every scenario. Omit to use each scenario's own default.
`scenarios`	No	Scenario IDs. Omit for every scenario in the bench.
`candidates`	Yes	1-4 model refs. Bare IDs use OpenRouter. Use anthropic:claude-... for 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.
`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.
`participant`	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.
`synthesizer`	No	Model ref used to reconcile judge scores. Omit for the default. Not used for one judge. 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
`plan`	Yes
`summary`	Yes
`estimate`	Yes
`warnings`	Yes
`readyToRun`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 17, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is known. The description nevertheless adds specific value beyond them: 'Makes no model calls and creates no run folder' and 'Free preview', which tell the agent the cost and side-effect surface more concretely than the generic hints do. It stops short of describing the response shape, but an output schema exists.

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

Conciseness5/5

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

Front-loaded with 'Free preview.' and three dense sentences that each carry weight: the no-side-effects constraint, the validation/defaults/plan behavior, and the workflow instruction. No filler.

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

Completeness4/5

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

For a 7-parameter tool with full schema coverage, annotations, and an output schema, the description covers purpose, side effects, cost, and workflow handoff adequately. Only minor gaps remain in describing what the preview output looks like, which the output schema likely handles.

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

Parameters3/5

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

Schema description coverage is 100%, so every parameter (bench, turns, scenarios, candidates, judgePanel, participant, synthesizer) is already documented with defaults, ranges, and the @level syntax. The description adds only the general notion that defaults get filled in, not per-parameter semantics. Baseline 3 is correct here.

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 verb and resource ('preview a tournament run') and enumerates what the preview does: validate against the model catalog, fill defaults, show plan, rough cost, and remaining provider setup. It cleanly separates itself from tournament_evaluate, the sibling that actually runs the tournament.

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?

Explicitly sequences the workflow: run this preview, show it, get the user's 'yes', and only then call tournament_evaluate. That is a concrete when-to-use rule tied to a named alternative, not implied guidance.

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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