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anrgctMCP SERVER

Autodev Codebase

Semantic search over your repository, indexed into Qdrant and served to your editor — with Ollama it never leaves the machine.

by anrgct ↗Code Intelligence & Repository IndexingCommunity

LABSCO SUMMARY

Local-first code search your editor can query.

The draw is that it can run end to end on your own hardware — Ollama for embeddings and reranking, Qdrant in a container — so the repository is never uploaded to index it. The MCP server is a thin surface over the CLI: index once, then the client searches by meaning, reads outlines, or checks how fresh the index is.

What it is

A vector-embedding code search tool that works as a CLI and as an HTTP MCP server. codebase index --serve starts the server at http://localhost:3001/mcp, and codebase stdio --server-url=http://localhost:3001/mcp bridges it for clients that need stdio. Parsing is Tree-sitter across 40+ languages, the vector store is Qdrant, and with Ollama for embedding and reranking the whole pipeline runs offline. It is a fork of Roo Code.

What you get

  • Semantic search over the indexed repository, returning the matching spans with their line ranges — search_codebase
  • Index statistics, so you can tell whether what the assistant is searching is current — get_codebase_stats
  • Configuration read and changed from the client rather than the shell — configure_codebase
  • Structured outlines of files — functions, classes and methods, optionally with generated summaries per symbol — outline_codebase
  • Call graph analysis from the CLI: a single function's call tree at default depth 3, connection paths between several functions at default depth 10, wildcard queries like --query="get*", and Cytoscape.js export with --viz or an interactive viewer with --open
  • Call graphs across TypeScript/JavaScript, Python, Java, C/C++, C#, Rust and Go
  • Search you can narrow: glob path filters with brace expansion, --limit up to a maximum of 50, and --min-score between 0 and 1
  • LLM reranking of results on a 0-10 scale, local through Ollama or via an OpenAI-compatible provider — rerankerEnabled, rerankerProvider, rerankerMinScore
  • Embedding providers to choose from: OpenAI, Ollama, Jina, Gemini, Mistral, OpenRouter and Vercel
  • File watching that updates the index as the code changes, and a dependency analysis cache the README credits with 10-50x faster re-analysis

Requirements

Install with npm install -g @autodev/codebase (1.0.1). Two services have to be up first: Ollama with an embedding model pulled (brew install ollama ripgrep, ollama serve, ollama pull nomic-embed-text) and Qdrant, which the README runs in Docker on ports 6333 and 6334 with qdrantUrl pointing at http://localhost:6333. ripgrep is installed alongside Ollama in the same step. Keys are only needed if you pick a hosted embedder — embedderOpenAiApiKey or embedderOpenAiCompatibleApiKey — and the Ollama path needs none. Settings resolve in order: CLI arguments, then ./autodev-config.json, then ~/.autodev-cache/autodev-config.json, then built-in defaults; persistent values go through codebase config --set. MIT licensed.

Setup effort

One command — npm install -g @autodev/codebase

See installation options