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    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.
    Last updated
    15
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    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.
    Last updated
    3
    27
    Apache 2.0
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    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.
    Last updated
    1
    MIT
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    quality
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    Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
    Last updated
    4
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    Persistent docs and memory for AI agents. Writespace is a collaborative markdown editor with a built-in MCP server — your model reads, writes, organizes, and searches a shared workspace while humans edit the same docs live. Drop the ranked full-text search straight in as RAG retrieval.
    Last updated
    MIT
  • F
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    quality
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    Exposes any SQLite database as read-only MCP tools for AI assistants, enabling listing tables, describing schemas, and running SELECT queries with filtering, ordering, and pagination.
    Last updated
  • A
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    A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.
    Last updated
    2
    MIT
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    A meta-MCP server that acts as a universal gateway, allowing users to discover and execute tools from thousands of other MCP servers through semantic search. It dynamically loads servers on demand and provides standardized functions for searching, discovering, and running tools across the entire MCP ecosystem.
    Last updated
    6