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polygraphso

Polygraph

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by polygraphso
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TypeScript

Server Quality Checklist

83%
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  • Latest release: v0.1.15

  • Disambiguation5/5

    The two tools target distinctly different objects: one reads published attestations for servers, the other grades skills. Their descriptions clearly differentiate their purposes and use cases, leaving no ambiguity.

    Naming Consistency4/5

    Both tool names follow an underscore-separated verb_noun pattern, but the verbs differ (verify vs. run) and the nouns are not parallel (attestation vs. skill_litmus). Minor inconsistency, but overall predictable.

    Tool Count3/5

    With only 2 tools, the server is at the lower end of what is reasonable. While the scope is focused, the inclusion of a third tool (e.g., run_litmus for servers) would improve coverage without bloat.

    Completeness2/5

    The description of verify_attestation mentions a run_litmus tool for server grading that is not present, and there is no tool for publishing attestations. These gaps mean agents cannot perform the full workflow implied by the server's purpose.

  • Average 4.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 181 commits in the last 12 weeks
    • 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 Apache 2.0.

  • 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.

  • This repository includes a glama.json configuration file.

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

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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

  • Behavior5/5

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

    Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds extensive behavioral context: it is a static read that does not execute scripts, is fast, and discloses limitations (runtime commands invisible). It also explains the separate 'quality' signal and optional LLM-judged axes. No contradiction with annotations.

    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 well-structured with front-loaded purpose and subsequent details. While every sentence adds value, it is somewhat verbose (multiple paragraphs). A slightly more concise presentation would improve score.

    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 tool with one parameter and no output schema, the description covers purpose, checks, limitations, and parameter format. It mentions the safety letter and quality signal but does not explicitly describe the return structure. Overall, it provides sufficient context for effective use.

    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 description coverage is 100% and explains skill_ref as a local path. The tool description adds further value by specifying the v1 format and explicitly stating that remote refs are not yet supported, clarifying usage constraints beyond the schema.

    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 that the tool grades a skill against the open static safety litmus, specifying the checks (S-01, S-03, S-04) and differentiating it from behavioral execution. It also distinguishes from sibling tools by focusing on skills, while 'run_litmus' is likely more general.

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

    Usage Guidelines4/5

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

    The description explains when to use the tool (to statically grade a skill) and notes that remote refs are not supported. It implies the tool is not a full safety assessment by stating it is 'NOT behavioral proof,' but does not explicitly list alternative tools or conditions 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.

  • Behavior5/5

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

    Annotations already declare readOnlyHint and idempotentHint. The description adds valuable context: it explains the tool does not run anything, describes return fields (grade, UID, CID, fingerprint), discloses that grade publishing is rolling out so 'not_available' is common, and clarifies error semantics. No contradiction.

    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 well-structured paragraph. It front-loads the core purpose, then explains results, error handling, and ends with a parameter example. Every sentence adds value without fluff.

    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?

    Despite no output schema, the description adequately explains return values (grade, UID, CID, fingerprint) and handles two special cases ('not_available' and 'lookup_failed'). Given the complexity of attestation verification, this is complete.

    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% with a description for 'server_ref', but the description adds a concrete example and explains it's a registry-prefixed identifier, going beyond the schema's generic description.

    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 reads a published polygraph grade without running anything, specifying the verb 'read' and the resource 'server's polygraph attestation'. It distinguishes from sibling 'run_litmus' by stating when to use each.

    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?

    Provides explicit guidance: use before trusting/paying, use 'run_litmus' if grade is not published, and explains how to interpret results like 'not_available' vs 'lookup_failed'. Also warns about needing to recompute live fingerprint to prevent rug pull.

    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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