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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: ref_search_documentation finds documentation resources, while ref_read_url reads the content of specific URLs. There is no overlap or ambiguity between searching and reading operations.

    Naming Consistency5/5

    Both tools follow a consistent 'ref_verb_noun' naming pattern with snake_case. The prefix 'ref_' provides clear namespace identification, and the verb-noun structure (search_documentation, read_url) is uniform and predictable.

    Tool Count3/5

    With only two tools, the server feels minimal but functional for its documentation search/read purpose. While it covers core workflows, the count is borderline thin—additional tools for filtering, saving, or managing searches might enhance completeness without overcomplication.

    Completeness4/5

    The tool set covers the essential documentation workflow: searching and reading. However, there are minor gaps, such as no tools for saving results, filtering searches, or managing cached content, which agents might need to work around for advanced use cases.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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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  "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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden but only mentions searching capabilities and a related tool. It fails to disclose critical behavioral traits like whether this is a read-only operation, potential rate limits, authentication needs for private resources, or what the search results look like (e.g., format, pagination).

    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 concise with two sentences that directly state the tool's function and a usage tip. It's front-loaded and avoids unnecessary words, though it could be slightly more structured for clarity.

    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 complexity of a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., search results format), how private resources are accessed, or error handling, leaving significant gaps for an AI agent to use it effectively.

    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?

    The input schema has 100% coverage, fully describing the single 'query' parameter with details on including language/framework names and the 'ref_src=private' option. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage.

    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's purpose as searching for documentation across web, GitHub, and private resources, which is specific and actionable. However, it doesn't explicitly differentiate from its sibling 'ref_read_url', which is for reading URL content rather than searching, so it misses full sibling distinction.

    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 by mentioning 'ref_read_url' for reading content, suggesting a workflow, but lacks explicit guidance on when to use this tool versus alternatives or any exclusions. It provides some context but no clear when/when-not rules.

    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 the full burden of behavioral disclosure. It states the tool reads content and converts it to markdown, but lacks details on error handling, rate limits, authentication needs, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

    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 highly concise and well-structured in two sentences. The first sentence states the core purpose, and the second provides usage context. There is no wasted language, making it front-loaded and efficient.

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

    Completeness3/5

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

    Given the tool's moderate complexity (reading and converting web content) and lack of annotations or output schema, the description is adequate but incomplete. It covers purpose and basic usage but omits behavioral details like error cases or output specifics, leaving room for improvement in completeness.

    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?

    The input schema has 100% description coverage, with the parameter 'url' documented as 'The URL of the webpage to read.' The description adds minimal value beyond this by specifying that the URL should come from 'ref_search_documentation' results, but does not provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.

    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's purpose: 'Read the content of a url as markdown.' It specifies the verb ('Read') and resource ('content of a url'), making the action explicit. However, it does not explicitly distinguish this tool from its sibling 'ref_search_documentation', which likely searches rather than reads content, so it misses full differentiation.

    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 provides clear context for when to use this tool: 'The entire exact URL from a Ref 'ref_search_documentation' result should be passed to this tool to read it.' This implies usage after obtaining a URL from the sibling tool, offering a workflow guideline. However, it does not specify when not to use it or alternatives, keeping it from a perfect score.

    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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  • 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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