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ad-github1

agent-eval-mcp

by ad-github1
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: running evaluations, comparing results, and applying deployment policy. There is no overlap.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (run_evaluation_suite, compare_regression, decide_canary), making them predictable.

    Tool Count4/5

    With 3 tools, the server covers the core evaluation workflow without being bloated. Slightly minimal but appropriate for a focused server.

    Completeness4/5

    The tools cover the main lifecycle: run suite, compare results, decide promotion. Minor gaps like suite management exist, but the core workflow is complete.

  • Average 2.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 6 commits in the last 12 weeks
    • No stable releases found
    • 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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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 and the description does not disclose whether the tool is destructive, requires authentication, or has side effects, which is critical for an AI agent.

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

    Conciseness3/5

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

    The description is very short but at the expense of clarity; it lacks the substance needed to be genuinely concise.

    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 no output schema, minimal param info, and no annotations, the description fails to provide a complete picture of the tool's behavior and context.

    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 coverage is 0% and the description does not explain what config_path or result_path refer to, leaving the agent without meaning for the parameters.

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

    Purpose3/5

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

    Description says 'Apply canary promotion policy to a suite result,' which gives a vague sense of purpose but lacks specificity on what the policy entails and how it differs from siblings.

    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 on when to use this tool versus siblings like compare_regression or run_evaluation_suite, leaving the agent without decision criteria.

    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 carries full burden. It only says 'compare', but doesn't disclose whether it's read-only, what side effects exist, or output format. For a comparison tool, it likely is read-only, but that is not stated.

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

    Conciseness2/5

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

    The description is extremely concise (6 words), but lacks structure and essential details. It is under-specified rather than efficiently concise.

    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 3 undocumented parameters, no output schema, and no annotations, the description is far from complete. It does not explain the comparison logic, output format, or parameter usage.

    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 coverage is 0%, and the description does not mention any parameters. It fails to add meaning beyond the schema, leaving three parameters (including required ones) undocumented.

    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 it compares two files (baseline and candidate suite JSON files), which is a specific verb+resource. It differentiates from siblings 'decide_canary' (decision) and 'run_evaluation_suite' (executing evaluations).

    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 on when to use this tool vs alternatives. No context on prerequisites 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 disclose behavioral traits. It only mentions running evaluations and emitting outputs, but fails to describe side effects, destructive actions, auth requirements, or performance implications. For a tool that likely modifies state or consumes resources, this is insufficient.

    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 sentence, no wasted words. It is front-loaded with the core action. However, it could benefit from a brief list of parameters or constraints without becoming verbose.

    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 6 parameters (1 required), no output schema, and no annotations, the description is severely incomplete. It does not explain parameter roles, output structure, error handling, or expected behavior after execution. An agent would likely misuse or misunderstand the tool.

    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?

    The input schema has 0% description coverage, and the tool description does not explain any parameter meanings, default values, or constraints. An agent cannot infer semantic intent for parameters like 'variant' or 'gate_path' from the name alone.

    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 tool's action ('Run') and resource ('RAG/wiki agent evaluation cases'), and specifies the output formats (JSON, Markdown, telemetry). This distinguishes it from sibling tools like 'compare_regression' and 'decide_canary', which have different purposes.

    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 on when to use this tool versus alternatives, nor any prerequisites or conditions. The description only states functionality, omitting context like when not to use it or comparison to siblings.

    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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