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wolfyy970

Docs Fetch MCP Server

by wolfyy970
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TypeScript
Remote

Server Quality Checklist

58%
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 single tool 'fetch_doc_content' has a clear and distinct purpose, making it impossible for an agent to confuse it with other tools.

    Naming Consistency5/5

    Since there is only one tool, it inherently follows a consistent naming pattern. The tool name 'fetch_doc_content' uses a verb_noun structure, which is clear and predictable, and there are no other tools to cause inconsistency.

    Tool Count2/5

    A single tool is too few for a server named 'Docs Fetch MCP Server', which implies a domain involving document fetching and exploration. While the tool is well-described, one tool feels thin and insufficient for comprehensive operations like managing fetched content or handling different document types, limiting the server's utility.

    Completeness2/5

    The tool surface is severely incomplete for the inferred domain of document fetching. It only provides a fetch operation with depth control, missing essential functions such as listing fetched documents, updating or deleting cached content, searching within documents, or handling errors, which are necessary for a complete workflow.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • This repository is licensed under MIT License.

  • 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

  • Behavior2/5

    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 describes the core functionality (fetching and exploring links) but lacks details on permissions, rate limits, error handling, or what the response looks like (e.g., format, size limits). This leaves significant gaps for a tool that interacts with external web resources.

    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 a single, efficient sentence that clearly states the tool's purpose and key feature (exploring linked pages). It is front-loaded with the main action and includes no redundant information, making it highly concise and well-structured.

    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 fetching web content with link exploration, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like authentication needs, rate limits, or response format, which are critical for effective tool use in this context.

    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 schema description coverage is 100%, so the schema already documents both parameters ('url' and 'depth') with descriptions and constraints. The description adds minimal value by implying the depth parameter relates to link exploration, but it doesn't provide additional syntax or format details beyond what the schema specifies.

    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 with a specific verb ('fetch') and resource ('web page content'), and it adds valuable context about exploring linked pages. However, since there are no sibling tools mentioned, it doesn't need to differentiate from alternatives, which keeps it from reaching a perfect score.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It mentions the ability to explore linked pages up to a specified depth, but this is more about functionality than usage 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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  • Evaluate tool definition quality.

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