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immber

ATproto Docs MCP Server

by immber

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a clear, distinct purpose focused on searching documentation.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'search_documentation' follows a clear verb_noun pattern.

    Tool Count2/5

    A single tool is too few for a server named 'ATproto Docs MCP Server', which suggests a documentation-focused domain. This minimal set lacks essential operations like browsing, retrieving specific sections, or navigating documentation structure.

    Completeness2/5

    The tool surface is severely incomplete for a documentation server. It only provides search functionality, missing basic operations such as listing documentation topics, getting detailed content, or accessing related resources, which are critical for comprehensive documentation interaction.

  • Average 3.6/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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 Creative Commons Zero v1.0 Universal.

  • This repository includes a README.md file.

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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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: 'Results are limited and returned as semantically similar chunks to the query.' This adds useful context about result limitations and format. However, it doesn't cover other important aspects like error handling, authentication needs, or rate limits, leaving gaps for a tool with no annotation support.

    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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds necessary context. Every sentence earns its place with no wasted words, making it efficient and easy to parse.

    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 (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It explains the purpose and result format but lacks details on error cases, authentication, or output structure. Without an output schema, more information on return values would be helpful, though the description does mention result limitations.

    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 has 1 parameter with 0% description coverage, so the description must compensate. It explains that the 'query' parameter is used to 'search the documentation' and 'answer questions about documentation,' providing clear semantic meaning beyond the schema. This adequately covers the single parameter, though it doesn't detail query syntax or examples.

    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: 'Search the documentation for the given query.' This specifies the verb ('search') and resource ('documentation'), making it easy to understand what the tool does. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, so it doesn't reach the highest score.

    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 provides some usage context: 'This tool can be used to answer questions about documentation.' This implies when to use it, but it doesn't offer explicit guidance on when not to use it or mention any alternatives. With no sibling tools, the guidance is adequate but not comprehensive.

    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.
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  • Evaluate tool definition quality.

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