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Dedalus MCP Documentation Server

by kitan23
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Python
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    • Dedalus MCP Documentation

      kbhuw
      A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables AI-powered querying and serving of markdown documentation with search, Q\&A capabilities, and document analysis. Built for the YC Agents Hackathon with OpenAI integration and rate limiting protection.
      MIT

    • Dedalus MCP Documentation

      kbhuw
      A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables serving and querying documentation with AI capabilities, allowing users to search, ask questions, and get AI-powered answers from their documentation files. Built for seamless deployment on the Dedalus platform with OpenAI integration for enhanced document analysis.
      MIT

    • Documentation MCP Server

      esola-thomas
      A
      license
      A
      quality
      C
      maintenance
      Enables AI assistants to navigate and query hierarchical documentation structures, supporting markdown files with YAML metadata and OpenAPI 3.x specifications. It features intelligent full-text search, metadata filtering, and a built-in web interface for both human and AI-driven documentation access.
      6
      MIT

    • okf

      aminHZDEV
      A
      license
      Not graded
      quality
      A
      maintenance
      Builds a keyword-searchable index from markdown documentation bundles and serves it to LLM agents via MCP, with retrieval logging and reporting for curation.
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.6/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: analyze_docs performs analysis tasks, ask_docs answers questions via AI, index_docs handles indexing, list_docs enumerates files, and search_docs performs keyword searches. The descriptions reinforce these distinct roles, making tool selection unambiguous for an agent.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with snake_case naming (e.g., analyze_docs, ask_docs, index_docs, list_docs, search_docs). This predictable structure enhances readability and usability, with no deviations or mixed conventions across the set.

    Tool Count5/5

    With 5 tools, this server is well-scoped for documentation management, covering core operations like listing, searching, indexing, analyzing, and querying. Each tool earns its place without redundancy, making the count appropriate for the domain's typical workflows.

    Completeness5/5

    The tool set provides complete coverage for documentation management, including CRUD-like operations (list, search, index) and advanced AI functionalities (analyze, ask). There are no obvious gaps; agents can perform end-to-end tasks from discovery to analysis without dead ends.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources