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"Using RAG to Provide Documentation to LLMs" matching MCP servers:

  • Skill-to-MCP

    biocontext-ai
    A
    license
    A
    quality
    C
    maintenance
    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
    3
    27
    Apache 2.0

  • Docs-to-AI

    dorianmeric
    A
    license
    Not graded
    quality
    C
    maintenance
    Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.
    1
    MIT

  • docs-to-mcp

    fayazara
    F
    license
    Not graded
    quality
    D
    maintenance
    A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.
    17

  • llms-mcp

    druellan
    A
    license
    Not graded
    quality
    D
    maintenance
    An MCP server that exposes the llms.txt file and its referenced local or external resources from a project root to provide context for AI models. It automatically parses documentation links and URLs to make them accessible as additional MCP resources.
    1
    MIT

  • MCP PDF to PGVector

    sanskritiparmendranaval
    F
    license
    Not graded
    quality
    C
    maintenance
    MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

  • Documentation Fetcher & RAG

    mikkelkrogsholm
    F
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.

  • llms-txt-mcp

    tenequm
    A
    license
    Not graded
    quality
    C
    maintenance
    Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.
    3
    MIT

  • NL-to-SQL MCP

    SatyamRudrakanthwar
    F
    license
    Not graded
    quality
    C
    maintenance
    MCP server enabling natural-language querying of SQLite databases via schema discovery, GraphRAG retrieval, and safely guarded read-only SQL execution.

  • Infra Ops Text to SQL

    Nirusha-19
    F
    license
    Not graded
    quality
    B
    maintenance
    Translates plain English questions about infrastructure operations into SQL queries, executes them against a database, and returns the real answer.

  • RAG Query MCP Server

    Akashbakshi99
    F
    license
    Not graded
    quality
    C
    maintenance
    Enables querying a hybrid-retrieval RAG pipeline (dense + BM25) over ingested PDF documents, returning answers generated by Gemini.

  • knowledge-rag

    lyonzin
    A
    license
    A
    quality
    A
    maintenance
    Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.
    13
    257
    MIT

  • rag-mcp

    NextLevelManagementAdvisors
    A
    license
    A
    quality
    B
    maintenance
    Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
    4
    AGPL 3.0