Overview
Features
Semantic code search context
Provides context from the entire codebase to Claude Code via MCP by retrieving relevant code chunks instead of loading whole directories.
Cost-efficient context provisioning
Stores code in a vector database and uses only related code in context to reduce token usage and costs.
Hybrid code search
Supports BM25 plus dense vector search to locate pertinent code efficiently.
Incremental indexing
Efficiently re-indexes only changed files using Merkle-tree-based change tracking.
Intelligent code chunking
Utilizes AST-based code chunking for more meaningful search units and results.
Scalable vector database integration
Works with Milvus or Milvus Cloud for scalable, managed vector search.
Flexible embedding providers
Supports OpenAI, VoyageAI, Ollama, Gemini embedding providers.
Broad MCP client compatibility
Follows the standard MCP protocol via stdio transport and works with numerous MCP clients and IDEs.
Who Is This For?
- AI developers:Integrate semantic code search context into Claude Code and other AI coding agents to improve search relevance and contextual accuracy.
- Software teams / engineers:Index and search large codebases to provide contextual code snippets to AI copilots and assistants.
- MCP client integrators / IDE vendors:Configure and deploy Claude Context MCP server across a broad set of MCP clients and development tools.




