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samalbanese

mcp-tournament

by samalbanese
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TypeScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: quick test runs a minimal scenario, leaderboard reads cached scores, and evaluate runs a full evaluation. No overlap or ambiguity.

    Naming Consistency2/5

    Naming is inconsistent: 'quick_test' uses underscore and adjective+noun, 'leaderboard' is a single noun without underscore, and 'evaluate' is a bare verb. No consistent pattern in structure or part of speech.

    Tool Count4/5

    With 3 tools, the server is at the low end of the typical 3-15 range but still reasonable for a focused evaluation service. The scope is narrow enough that each tool earns its place.

    Completeness4/5

    The tools cover the core workflow: quick test, full evaluation, and reading results. Minor gaps exist, such as no tool for configuring judges or scenarios, but these are likely predefined or managed externally.

  • Average 2.8/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • No commit activity data available
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    No annotations are provided, so the description must fully disclose behavior. It states 'Read the best cached score' implying a read-only operation, but does not mention potential side effects, authentication requirements, or data source details beyond 'result files'.

    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?

    The description is a single concise sentence with no unnecessary words. It is well-structured and front-loaded with the key action.

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

    Completeness2/5

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

    Despite having only 2 parameters and no output schema, the description lacks details about return format, data structure, or how parameters affect results. The 'plugin' parameter is left entirely unexplained.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description does not explain the purpose or valid values of the 'limit' and 'plugin' parameters. The agent cannot infer parameter semantics from the description alone.

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

    Purpose4/5

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

    The description clearly states the tool reads cached scores per model from result files, using a specific verb and resource. It distinguishes itself from sibling tools quick_test and evaluate by focusing on cached leaderboard data.

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

    Usage Guidelines2/5

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

    No guidance is given on when to use this tool versus alternatives like tournament.quick_test or tournament.evaluate. The agent has no context about preferred use cases.

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

  • Behavior2/5

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

    Without annotations, the description should fully disclose behavioral traits. It mentions specifics like single scenario/judge and no synthesis call, but it does not indicate whether the operation is safe (e.g., read-only), destructive, or has other side effects.

    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?

    The description is a single concise sentence with no unnecessary words. It front-loads the core action and key constraints ('one scenario', 'one judge', 'no synthesis model call').

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

    Completeness1/5

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

    Given the tool has 3 parameters (none with schema descriptions), no output schema, and no annotations, the description is severely incomplete. It fails to explain parameters, return values, or usage context, leaving an agent unable to invoke the tool correctly.

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

    Parameters1/5

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

    Schema description coverage is 0%, so the description must explain parameter meanings. The description only mentions 'one scenario' and 'one judge' but does not map to the parameters 'model', 'plugin', or 'scenario', nor does it clarify their roles or constraints.

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

    Purpose4/5

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

    The description clearly states the tool performs a quick test with one scenario, one judge, and no synthesis call. This distinguishes it from sibling tools like tournament.leaderboard and tournament.evaluate, which imply broader or more comprehensive evaluation.

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

    Usage Guidelines2/5

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

    No explicit guidance on when to use this tool versus alternatives. The description implies it is for quick tests but does not state prerequisites, limitations, or when not to use it.

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

  • Behavior2/5

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

    No annotations are provided, so the description must cover behavioral aspects. It only states 'evaluate with a judge panel' without disclosing whether the operation is read-only, what side effects occur, authentication needs, or rate limits. This is insufficient for a tool that likely performs comparative analysis.

    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?

    The description is a single concise sentence that front-loads the verb and resource with no extraneous words. Every part is useful.

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

    Completeness2/5

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

    Given the tool has 4 parameters, no output schema, and no parameter descriptions, the description is severely lacking. It does not explain what the evaluation returns, how results are presented, or what 'plugin' and 'scenarios' entail, leaving significant gaps for the AI agent.

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

    Parameters2/5

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

    The input schema has 0% description coverage, and the description adds minimal parameter insight. It mentions 'judge panel' which relates to the 'judges' parameter but does not explain 'plugin' or 'scenarios'. The meaning of parameters beyond their names is largely opaque.

    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?

    The description clearly states the verb 'evaluate' and the resource 'one to four candidate models' with a 'judge panel'. It distinguishes from sibling tools like quick_test and leaderboard by specifying the use of a judge panel for evaluation.

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

    Usage Guidelines3/5

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

    The description implies usage for evaluating models with a panel but provides no explicit guidance on when to use this tool versus alternatives like quick_test or leaderboard. No when-not-to-use or prerequisite information is given.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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