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Server Quality Checklist

67%
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  • Latest release: v0.7.1

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity or overlap. The tool's purpose is clearly defined as fetching markdown from URLs.

    Naming Consistency5/5

    The single tool name 'fetch_markdown' follows a clear verb_noun pattern, which is internally consistent and readable.

    Tool Count4/5

    With only one tool, the count is minimal, but it matches the server's narrow purpose of fetching markdown. It is slightly under the typical range but not insufficient.

    Completeness4/5

    The tool covers the core operation of fetching markdown, including options for raw output and file saving. Minor non-essential features like batch fetching are absent, but no critical gaps for the stated scope.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 19 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 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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  • 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

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses specific error outcomes (unsupported_content_type, extraction_failed), file-write behavior (overwrites, parent must exist, sandboxing, save_forbidden), and guarantees (fetch errors never touch the file). This is exceptional transparency beyond basic operation.

    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 four sentences, front-loaded with purpose, followed by key behavioral caveats and parameter guidance. Every sentence earns its place; there is no redundancy or filler. It is dense but highly readable.

    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?

    Even without annotations or an output schema, the description is remarkably complete for a tool of this complexity. It covers purpose, use cases, error conditions (unsupported_content_type, extraction_failed), parameter effects, file-write security, and the raw bypass. No critical ambiguity remains for an agent to invoke it correctly.

    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?

    The input schema already has 100% coverage with rich descriptions for all three parameters. The description adds meaningful context, such as 'Non-HTML responses return unsupported_content_type unless raw is set' and 'Set savePath to write the output to a file instead of returning it inline,' which reinforces and extends schema semantics. Baseline is 3 due to high coverage; the added value warrants a 4.

    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 opens with a specific action ('Fetch a public HTTP/S URL') and a clear output ('main article content as clean markdown'). It lists target use cases (articles, documentation, blog posts, reference pages), which distinguishes it from generic fetch tools. The verb and resource are explicit and unambiguous.

    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 explicitly states when to use the tool via 'Best for articles, documentation, blog posts, and reference pages.' It also implies when to use raw mode ('Non-HTML responses return unsupported_content_type unless raw is set') but does not provide formal when-not-to-use guidance or alternative tool names. This is clear context without exclusions.

    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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