An MCP server for semantic code search & navigation that helps AI agents work efficiently without burning through costly tokens. Instead of reading entire files, agents can search conceptually and jump directly to the specific functions, classes, and code chunks they need.
A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.
An MCP server that indexes reference repositories and provides tools for AI coding agents to retrieve lossless code context, enabling reasoning over codebases larger than the agent's context window.
A local-first MCP server that provides AI agents with safe codebase access through file discovery, hybrid lexical-semantic search, and project introspection. It features durable local memory and semantic indexing while keeping all data and processing entirely on your local machine.
A local-first MCP server that lets AI assistants search and retrieve context from indexed projects, Git state, decisions, and tasks without sending data to the cloud.