Entwickler-Tools
context-engineering-intro
Context engineering is the new vibe coding - it's the way to actually make AI coding assistants work. Claude Code is the best for this so that's what this re…
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Anleitung ansehen →claude-context ist ein Open-Source-Projekt aus der Kategorie Entwickler-Tools von zilliztech (zilliztech/claude-context auf GitHub). Es hat 12.594 Sterne auf GitHub und 943 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 14. Juli 2026 auf GitHub aktualisiert.
Code-search MCP for Claude Code: make your entire codebase the context for any coding agent.
Sign up for a free Zilliz Cloud vector database and copy its endpoint and token, and get an OpenAI API key for the embedding model.
Register claude-context with your coding agent using the provided command or JSON config, passing the OpenAI key and Milvus address/token as environment variables.
Restart the client, open your project, and ask the agent to 'Index this codebase'; watch progress with the indexing-status tool.
Ask things like 'Find functions that handle user authentication' and let hybrid search pull the relevant code into the agent's context.
Finds all relevant code across an entire codebase in one query, instead of agents guessing file paths or loading whole directories.
Combines BM25 keyword matching with dense-vector semantic search for both exact identifiers and conceptual matches.
index_codebase, search_code, clear_index and get_indexing_status cover the whole lifecycle from your agent chat.
Documented setups for Claude Code, Codex CLI, Gemini CLI, Qwen Code, Cursor, VS Code, Windsurf, Cline, Roo Code, Claude Desktop and more - any stdio MCP client works.
Only related code enters the model's context; the project's evaluation reports roughly 40% token reduction at equivalent retrieval quality.
Choose embedding models (for example OpenAI text-embedding-3-large or VoyageAI voyage-code-3) and customize file inclusion and exclusion rules.
Clone Claude Context if you want the source or the VS Code extension code; most users run the published MCP package via npx.
git clone https://github.com/zilliztech/claude-context.git
cd claude-contextRegister the server with your embedding key and Milvus/Zilliz credentials as environment variables.
claude mcp add claude-context -e OPENAI_API_KEY=your-openai-api-key -e MILVUS_ADDRESS=your-zilliz-cloud-public-endpoint -e MILVUS_TOKEN=your-zilliz-cloud-api-key -- npx @zilliz/claude-context-mcp@latestRestart your MCP client, open the project in your agent, and ask it to index the codebase, then search in natural language.
# In your agent chat: "Index this codebase" - see README for detailsIt is developed by zilliztech, the team behind Milvus and Zilliz Cloud, with community contributors.
The code is MIT-licensed and free. The suggested setup uses a free Zilliz Cloud database, but embeddings use your own OpenAI API key, which is pay-as-you-go.
You need an embedding provider key (OpenAI by default; VoyageAI and others can be configured) and a Milvus instance - Zilliz Cloud's free tier is the documented starting point.
Grep matches exact text; Claude Context adds semantic, meaning-based retrieval with hybrid ranking over an index, so agents find conceptually related code that shares no keywords.
Get a free Zilliz Cloud key and an OpenAI key, run the claude mcp add command from the README, restart Claude Code, and say 'Index this codebase'.
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