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MCP-Mirror

Library Docs MCP Server

by MCP-Mirror
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Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_docs' has a clear, distinct purpose of searching documentation for specified libraries.

    Naming Consistency5/5

    The single tool name 'get_docs' follows a clear verb_noun pattern. With only one tool, naming consistency is inherently perfect as there are no other tools to compare against.

    Tool Count2/5

    A single tool is insufficient for a documentation search server. While the tool covers basic search functionality, the server lacks tools for browsing documentation structure, getting library metadata, or handling multiple queries efficiently, making the tool count too low for the apparent scope.

    Completeness2/5

    The tool surface is severely incomplete for a documentation server. There are significant gaps: no ability to list available libraries, browse documentation hierarchies, get version-specific docs, or handle pagination/search refinement. The single search tool provides only basic functionality without supporting common documentation workflows.

  • Average 3.4/5 across 1 of 1 tools scored.

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

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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?

    No annotations are provided, so the description carries the full burden. It mentions the action ('search') and returns 'Text from the docs,' but fails to disclose key behavioral traits such as search scope (e.g., full-text, title-only), result limits, error handling, or authentication needs. This leaves significant gaps for an agent to understand the tool's 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a concise list of supported libraries and clear sections for Args and Returns. Every sentence adds value without redundancy, making it efficient and well-structured.

    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 (2 parameters, no annotations, no output schema), the description is minimally adequate. It covers the purpose and parameters but lacks details on behavioral aspects like search behavior or output format. Without annotations or output schema, more context on how results are returned would improve completeness.

    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 description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'query' is for searching (e.g., 'Chroma DB') and 'library' specifies the target (e.g., 'langchain'), including a list of supported libraries. This compensates well for the schema's lack of descriptions, though it could provide more detail on parameter constraints.

    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 docs for a given query and library.' It specifies the verb ('search') and resource ('docs'), and mentions the supported libraries. However, without sibling tools, it cannot differentiate from alternatives, so it doesn't reach a perfect score of 5.

    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 listing supported libraries ('langchain, llama-index, mcp, and openai'), which suggests when to use it based on library compatibility. However, it lacks explicit guidance on when not to use it or alternatives, and there are no sibling tools to compare against, so the guidance is limited to implied context.

    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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