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

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    Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.
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    115
    MIT
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    A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.
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    MIT
  • F
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    A TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.
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    Enables LLM agents to efficiently understand and navigate a codebase by providing semantic search over symbols and a reference graph, replacing expensive grep/glob calls with structured tools like definition lookup, caller/callee queries, and change-impact analysis.
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    MIT
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    An MCP server that enables users to query Kedro framework documentation using retrieval-augmented generation. It builds a local knowledge base from documentation files to help users navigate and apply Kedro's data science pipeline framework.
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    Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.
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    MIT
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    Transforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.
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    MIT
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    AI modifies a function but misses 5 callers that also need updating. crag fixes this — it builds precise call graphs via static analysis, so AI knows exactly what's affected before making changes.
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    A powerful Model Context Protocol server that creates intelligent graph representations of your codebase with comprehensive semantic analysis capabilities, supporting 11 languages and 26 MCP methods.
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    MIT
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    Enables context-aware semantic search across codebases using Qdrant vector database with intelligent GitHub issue resolution, Projects V2 management, and progressive context retrieval for 95%+ token reduction in AI-assisted development.
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    Provides access to Godot engine documentation through a Retrieval-Augmented Generation (RAG) system using ChromaDB. It enables users to query processed Godot documentation and technical chunks for development assistance within MCP-compatible environments.
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    MIT
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    Semantic code search for AI agents — hybrid vector + BM25 with cross-encoder reranking, AST-aware chunking for 14 languages, conversation memory, code annotations, and search analytics. Zero config, zero API keys. Just bunx
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    Apache 2.0
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    Provides AI agents with instant access to official Apple developer documentation, Swift programming guides, design guidelines, and Apple Developer YouTube content including WWDC sessions. Uses advanced RAG technology with semantic search and AI reranking to deliver accurate, contextual answers for Apple platform development.
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    An MCP server that generates AI agent tools from Postman collections and requests. This server integrates with the Postman API to convert API endpoints into type-safe code that can be used with various AI frameworks.
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    MIT