Which integrations are available for this server?

Enriches code chunks with authorship, timestamps, churn metrics, and task IDs extracted from commit history and git blame data. Supports extracting GitHub task IDs from commit messages to provide context and linking between code and project issues. Enables extraction of JIRA task IDs from commit messages to associate indexed code chunks with specific project tickets. Integrates with Ollama for local, privacy-first embedding generation and semantic codebase search. Supports OpenAI embedding models for semantic vectorization and high-performance code search. Provides specialized Ruby AST-aware chunking to improve the accuracy and relevance of semantic search in Ruby codebases.

How do I use Tea Rags MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Tea Rags MCP search for where user authentication is implemented" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Tea Rags MCP by artk0de

MCP compatible quickstart < 15 min local-first reproducible: docker provider agnostic

codecov

MCP server for semantic code search with git trajectory reranking. AST-aware chunking, incremental indexing, millions of LOC. Reranks results using authorship, churn, bug-fix rates, and 19 other signals — not just embedding similarity. Built on Qdrant. Works with Ollama (local) or cloud providers (OpenAI, Cohere, Voyage).

📖 Full documentation — 15-minute quickstart, agent workflows, architecture deep dives.

🧬 Trajectory Enrichment

Standard code RAG retrieves by similarity alone. Trajectory enrichment augments each chunk with signals about how code evolves — at the function level, not just file level.

🔀 Git trajectory — churn, authorship, volatility, bug-fix rates, task traceability. 19 signals feed composable rerank presets (hotspots, ownership, techDebt, securityAudit...)
🕸️ Topological trajectory (planned) — symbol graphs, cross-file coupling, blast radius

Opt-in via CODE_ENABLE_GIT_METADATA=true. Without it — standard semantic search with AST-aware chunking.

💡 An agent can find stable templates, avoid anti-patterns, match domain owner's style, and assess modification risk — all backed by empirical data. Read more →

🚀 Quick Start

git clone https://github.com/artk0de/TeaRAGs-MCP.git cd TeaRAGs-MCP npm install && npm run build # Start Qdrant + Ollama podman compose up -d podman exec ollama ollama pull unclemusclez/jina-embeddings-v2-base-code:latest # Add to Claude Code claude mcp add tea-rags -s user -- node /path/to/tea-rags-mcp/build/index.js \ -e QDRANT_URL=http://localhost:6333 \ -e EMBEDDING_BASE_URL=http://localhost:11434

Then ask your agent: "Index this codebase for semantic search"

📚 Documentation

artk0de.github.io/TeaRAGs-MCP

	Section	What's inside
🏁	Quickstart	Installation, first index & query
⚙️	Configuration	Env vars, providers, tuning
🤖	Agent Integration	Prompt strategies, generation modes, deep analysis
🏗️	Architecture	Pipeline, data model, reranker internals

🤝 Contributing

See CONTRIBUTING.md for workflow and conventions.

🙏 Acknowledgments

Built on a fork of mhalder/qdrant-mcp-server — clean architecture, solid tests, open-source spirit. And its ancestor qdrant/mcp-server-qdrant. Code vectorization inspired by claude-context (Zilliz).

Feel free to fork this fork. It's forks all the way down. 🐢