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AI/TLDR

Claude Context

MCP plugin that gives Claude Code semantic search over your whole codebase

github.com/zilliztech/claude-context★ 11.9kgithub.com/zilliztech/claude-context/tree/master/docs

Overview

Claude Context is an MCP plugin from Zilliz that adds semantic code search to Claude Code and other AI coding agents. Instead of making the agent read folders file by file, it lets the agent find all relevant code across millions of lines in one step and pulls those results straight into the model's context.

It works by indexing your codebase into a vector database such as Milvus or the managed Zilliz Cloud. Embeddings are produced by a provider you choose, and only the related code is sent to the model on each request. For large projects this keeps token usage and cost much lower than loading entire directories every time.

What it does

  • Semantic, natural-language code search that finds relevant snippets by meaning rather than exact keywords
  • Hybrid search that combines BM25 keyword matching with dense vector search for better results
  • Incremental indexing that uses Merkle trees to re-index only the files that changed
  • Pluggable embedding providers: OpenAI, VoyageAI, Ollama, and Gemini
  • Stores vectors in Milvus or the fully managed Zilliz Cloud service
  • Works across many MCP clients including Claude Code, Cursor, Gemini CLI, Codex CLI, Windsurf, and Claude Desktop

Getting started

Claude Context runs as an MCP server. You need Node.js 20 or later, a vector database (a free Zilliz Cloud account works), and an embedding API key such as an OpenAI key. The quickest path is to register the server with the Claude Code CLI.

Add the MCP server to Claude Code

Use the Claude Code CLI to register Claude Context, passing your embedding key and vector database connection as environment variables. The server is run on demand via npx, so there is nothing to install globally.

bashbash
claude mcp add claude-context \
  -e OPENAI_API_KEY=sk-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@latest

Index your codebase

Once the server is connected, ask your agent to index the project. The index_codebase tool builds a hybrid (BM25 + dense vector) index of your code in the vector database.

texttext
Index this codebase

Check the indexing status

Indexing large repositories takes time. Ask the agent to report progress; the get_indexing_status tool shows a percentage while indexing and a completion status when done.

texttext
Check the indexing status

Search your code in natural language

After indexing finishes, ask questions in plain language. The search_code tool returns the most relevant snippets and feeds them into the agent's context so it can answer with real code from your project.

texttext
Where is the user authentication handled in this codebase?

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

When to use it

  • Give Claude Code deep context on a large codebase without manually copying files into the prompt
  • Cut token costs by retrieving only the code that is relevant to each request instead of loading whole directories
  • Help an AI agent quickly locate where a feature, function, or bug lives across millions of lines
  • Add the same semantic code search to other MCP clients like Cursor, Windsurf, Gemini CLI, or Codex CLI

How Claude Context compares

Claude Context alongside other open-source rag frameworks & platforms tools AI/TLDR tracks, ranked by GitHub stars.

ToolStarsWhat it does
Dify★ 146kAn open-source platform with a visual workflow builder for creating LLM and RAG applications without writing much code.
RAGFlow★ 83.3kA RAG engine built around deep document understanding that turns complex files into a grounded, citation-backed question-answering layer.
Context7★ 57.8kContext7 pulls current, version-specific documentation and code examples for any library and feeds them into your LLM, available as a CLI skill or an MCP server.
Quivr★ 39.2kQuivr is an open-source RAG framework that ingests your documents and answers questions about them, working with any LLM and any file type.
LightRAG★ 36.8kA graph-based RAG system that builds an entity-and-relationship knowledge graph for fast retrieval and easy incremental updates.
GraphRAG★ 33.9kMicrosoft's graph-based RAG system that extracts a knowledge graph from documents to answer broad, multi-document questions.
PageIndex★ 33.3kPageIndex turns long PDFs into a table-of-contents tree and uses LLM reasoning to retrieve relevant sections, with no vector database and no chunking.
Claude Context★ 11.9kMCP plugin that gives Claude Code semantic search over your whole codebase