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"Indexing a codebase for accessible contextual queries" matching MCP servers:

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    A Model Context Protocol server that provides Retrieval-Augmented Generation capabilities using Contextual AI, enabling AI interfaces like Cursor IDE and Claude Desktop to query domain-specific knowledge with context-aware responses and source citations.
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    Provides RAG (Retrieval-Augmented Generation) capabilities via Contextual AI, enabling query processing and context-aware responses with citations. Integrates with MCP clients like Cursor IDE and Claude Desktop.
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    AST-aware codebase indexing with semantic search, exposed as an MCP server. Enables semantic search and file context retrieval across your codebase using natural language queries.
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    MIT
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    Enables semantic code search across multiple repositories using AST-aware chunking and relationship tracking. Supports local LLM embeddings, real-time indexing, and cross-codebase dependency analysis through vector and graph databases.
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    MIT
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    Enables AI assistants to semantically search and understand code repositories using PostgreSQL with pgvector embeddings. Provides repository indexing, natural language code search, and development task management with git integration.
    MIT
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    Enables AI agents to semantically search and navigate code repositories using natural language, with support for multiple repos, incremental indexing, and no local install needed.
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    A minimalist indexing tool that provides AI agents with semantic search and structural AST parsing for deep codebase understanding. It enables autonomous agents to navigate large codebases predictably using vector embeddings and native language server capabilities like definition and reference tracking.
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    A semantic codebase indexer MCP server that chunks source code, generates embeddings via Ollama, and stores them in Qdrant for natural-language code search.
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    Enables semantic search across your codebase using Google's Gemini embeddings and Qdrant Cloud vector storage. Supports 15+ programming languages with smart code chunking and real-time file change monitoring.
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    MIT
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    Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.
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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.
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    MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.
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    MIT