Claude Context is an MCP (Model Context Protocol) plugin developed by Zilliz that enables semantic code search capabilities for Claude Code and other AI coding agents. The tool addresses a fundamental challenge in AI-assisted development: providing language models with relevant context from large codebases without incurring prohibitive token costs. By leveraging vector database technology and semantic search, Claude Context allows developers to make their entire codebase available as context for coding tasks without needing to load entire directories into each request.
The repository is written in TypeScript and serves as both a practical tool and a reference implementation for integrating semantic search into AI coding workflows.
The core functionality centers on three main capabilities. The index_codebase tool processes a codebase directory for hybrid search combining BM25 keyword matching with dense vector embeddings. The search_code tool enables natural language queries against the indexed codebase using the same hybrid search approach. The get_indexing_status tool provides visibility into indexing progress, while clear_index allows users to reset indices as needed. The system requires a vector database backend, with Zilliz Cloud offered as the primary option, and supports multiple embedding model providers including OpenAI and VoyageAI.
Claude Context distinguishes itself through broad integration support across numerous AI coding environments. The documentation details configuration procedures for Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, Cline, Augment Code, Roo Code, Cherry Studio, Void, and several other platforms. This extensive compatibility reflects the project's goal of becoming a standard tool across the AI coding ecosystem. The configuration approach varies by platform, with some supporting direct JSON configuration while others provide graphical interfaces for MCP server setup.
The project includes evaluation data demonstrating approximately 40 percent token reduction while maintaining equivalent retrieval quality. This efficiency gain translates directly to reduced API costs and faster response times in production environments. The evaluation methodology and detailed results are documented in the repository's evaluation directory, providing transparency about performance claims.
The architecture employs a hybrid search strategy combining traditional keyword-based retrieval with semantic vector search, allowing the system to capture both exact matches and conceptually related code. File inclusion and exclusion rules are configurable, enabling users to customize which files get indexed based on their project structure. The system requires Node.js version 20.0.0 or higher and integrates with external services including OpenAI for embeddings and Zilliz Cloud for vector storage.
The repository also references a companion project called memsearch, which extends Claude Code with persistent memory capabilities across sessions, suggesting an ecosystem approach to enhancing AI agent capabilities. The project's classification across multiple domains including context management, prompt engineering, token optimization, and semantic search reflects its multifaceted role in modern AI-assisted development workflows.
