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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.2.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity in tool selection; the tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    A single tool trivially follows a consistent naming pattern, as there are no other tools to compare or conflict with.

    Tool Count3/5

    One tool is borderline for a documentation-fetching server; while it may suffice for a narrow use case, typical servers offer multiple operations like listing sources or managing configurations.

    Completeness2/5

    The server lacks tools for managing documentation sources, such as adding, updating, or listing available URLs, which are significant gaps for a comprehensive documentation assistant.

  • Average 3.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior3/5

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

    The description states it returns 'raw, highly relevant text blocks,' implying a read-only, retrieval operation. No annotations are provided, so the description carries the full disclosure burden. It does not mention any destructive actions, side effects, or additional behavioral details like pagination, authentication, or rate limits, but the basic non-destructive nature is clear.

    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 concise with two sentences, the first stating the purpose and the second the output. It is front-loaded and contains no unnecessary words. Every sentence earns its place.

    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 has 2 parameters, no annotations, and an output schema (not provided), the description adequately covers the core functionality but lacks details on prerequisites, error handling, or output format. The presence of an output schema partially offsets the need for return value explanations, but the description does not reference it, leaving some gaps for agent understanding.

    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 0% coverage for parameter descriptions. The description adds meaning by implying 'url' is a documentation URL and 'query' is a search query, with the default query provided. However, it doesn't elaborate on query syntax or format. This adds some value but does not fully compensate for the lack of schema descriptions.

    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 fetches reference information, code blocks, and context from documentation URLs, specifying the resource type (programming framework, API, library, website) and the output (raw text blocks). It uses a specific verb ('fetch') and resource, making the purpose unambiguous. With no siblings, differentiation is not needed, but the description is clear enough.

    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 includes an explicit 'use this tool to fetch' directive, indicating when to use it. However, it does not provide alternatives or exclusions, such as when not to use it or what other tools might be better suited for different tasks. Given no siblings, this is acceptable but minimal, lacking depth for optimal guidance.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

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