Claude Context

What is Claude Context?

Claude Context is an MCP server that provides semantic code search and context retrieval for large codebases. It indexes your entire codebase using a vector database (Zilliz Cloud) and allows developers to query code with natural language instead of keyword matching.

How to use Claude Context?

Configure the server with your codebase path, Zilliz Cloud credentials, and optional file extension/ignore patterns. Then use MCP to send semantic search queries (e.g., "find functions that handle user authentication") and receive relevant code chunks in response.

Key features of Claude Context

• Semantic code search using natural language queries
• Incremental indexing via Merkle trees (only re-indexes changed files)
• AST-based intelligent code chunking for precise context
• Scalable vector storage with Zilliz Cloud integration
• Customizable file extensions, ignore patterns, and embedding models
• Context-aware discovery of relationships across millions of lines of code

Use cases of Claude Context

• Quickly find relevant functions or classes in a large legacy codebase
• Understand how different parts of a monorepo relate to each other
• Provide rich, context-aware code to an LLM without loading entire directories

FAQ from Claude Context

How does Claude Context differ from plain grep or keyword search?

It uses semantic search (vector embeddings) to retrieve code based on meaning, not exact keyword matches, catching relevant code that keyword search would miss.

Does it work with very large codebases?

Yes. It integrates with Zilliz Cloud for scalable vector search, and incremental indexing ensures only changed files are re-processed.

What happens when I modify a file in my codebase?

Claude Context uses Merkle trees to detect changes and performs incremental re-indexing, so only the modified portions are updated without a full rebuild.

What configuration options are available?

You can configure which file extensions to include, set ignore patterns for directories or files, and choose the embedding model used for vectorization.