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"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:

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    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
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    MIT
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    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.
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    AGPL 3.0
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    Searches local markdown files using TF-IDF retrieval and serves full documents as resources.
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    A local MCP server that enables LLMs to securely search and read files within designated Google Drive folders. Supports Google Docs, Google Sheets, PDFs, and plain text with strict folder-scoping via service account authentication.
    4
    MIT
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    Rust-powered PDF toolkit over MCP: create, read, and analyze PDFs; extract text and entities for RAG; convert to Markdown; split/merge/rotate/reorder pages; manage form fields and annotations; encrypt documents. Runs locally via uvx oxidize-mcp.
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    MIT
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    This MCP server provides comprehensive Google Workspace integration (Gmail, Drive, etc.) with multi-user OAuth. Unique features include RAG-powered natural language to Chat card rendering, runtime Jinja2 macro creation, and dynamic tool management.
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    Apache 2.0
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    A bridge connecting AI agents to NCBI's PubMed database through the Model Context Protocol, enabling seamless searching, retrieval, and analysis of biomedical literature and data.
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    Apache 2.0
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    A Model Context Protocol server that retrieves detailed geographic information about IP addresses using the ipinfo.io API, helping determine user location and network details.
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    MIT
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    Local-first RAG MCP server that indexes local documents and provides hybrid search (semantic + keyword) with file ingestion, URL fetching, and a CLI, keeping data on-device.
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    MIT
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    Context+ is a semantic intelligence server that transforms codebases into searchable, hierarchical feature graphs using RAG, Tree-sitter AST, and spectral clustering. It provides tools for deep code discovery, blast radius analysis, and memory graph management for high-accuracy engineering tasks.
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    MIT
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    Enables version-controlled golden dataset management and semantic evaluation for RAG/LLM pipelines using TF-IDF cosine similarity, without requiring an LLM API key.
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    MIT
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    Enables searching and retrieving CAIE past-paper questions with filters for subjects, years, and specific topics. It provides LLM-friendly responses including concise text previews and structured JSON data for single or multi-topic queries.
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    An MCP Server implementation that integrates the Balldontlie API, to provide information about players, teams and games for the NBA, NFL and MLB.
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    MIT
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    MCP-NixOS is a Model Context Protocol server that provides real-time, accurate information about NixOS packages, options, Home Manager, and nix-darwin configurations, preventing AI assistants from hallucinating about NixOS resources and enabling them to deliver factual system configuration guidance.
    2
    788
    MIT