A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
A Model Context Protocol (MCP) server that provides code analysis capabilities using tree-sitter, designed to give Claude intelligent access to codebases with appropriate context management.
Indexes your project's markdown documentation and exposes it to AI agents via local hybrid search (lexical + semantic) with progressive disclosure tools.
The open retrieval layer for AI agents. Index your entire project — code, docs, legal, research, data — and serve surgical context via MCP. FTS5 full-text search, optional semantic search (FastEmbed/ONNX), 10 built-in parsers, incremental auto-sync.
Local-first MCP server providing semantic search over library docs, fully offline. Single Go binary speaks MCP over stdio against a vector index pinned to the binary version. Like Context7 with the internet turned off. Apache 2.0. Linux + macOS, also available as a container image.
A local code indexing and search library that enables AI agents to perform semantic and keyword searches across codebases using tree-sitter and SQLite. It provides tools for indexing projects and finding precise code definitions without requiring external APIs, Docker, or server infrastructure.
Local-first context retrieval engine that serves precise documentation chunks to coding agents via MCP, ensuring high-confidence context for code generation.
