Claude Context
About
Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.
Details
- Author
- zilliztech
- Downloads
- 547
- Categories
- AI
Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Claude ContextCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
index_codebase
Index a codebase directory to enable semantic search using a configurable code splitter. ⚠️ **IMPORTANT**: - You MUST provide an absolute path to the target codebase. ✨ **Usage Guidance**: - This tool is typically used when search fails due to an unindexed codebase. - If indexing is attempted on an already indexed path, and a conflict is detected, you MUST prompt the user to confirm whether to proceed with a force index (i.e., re-indexing and overwriting the previous index).
search_code
Search the indexed codebase using natural language queries within a specified absolute path. ⚠️ **IMPORTANT**: - You MUST provide an absolute path. 🎯 **When to Use**: This tool is versatile and can be used before completing various tasks to retrieve relevant context: - **Code search**: Find specific functions, classes, or implementations - **Context-aware assistance**: Gather relevant code context before making changes - **Issue identification**: Locate problematic code sections or bugs - **Code review**: Understand existing implementations and patterns - **Refactoring**: Find all related code pieces that need to be updated - **Feature development**: Understand existing architecture and similar implementations - **Duplicate detection**: Identify redundant or duplicated code patterns across the codebase ✨ **Usage Guidance**: - If the codebase is not indexed, this tool will return a clear error message indicating that indexing is required first. - You can then use the index_codebase tool to index the codebase before searching again.
clear_index
Clear the search index. IMPORTANT: You MUST provide an absolute path.
get_indexing_status
Get the current indexing status of a codebase. Shows progress percentage for actively indexing codebases and completion status for indexed codebases.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"claude context": {
"claude-context": {
"command": "npx",
"args": [
"@zilliz/claude-context-mcp@latest"
],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}
}
}McpServers
{
"claude-context": {
"command": "npx",
"args": [
"@zilliz/claude-context-mcp@latest"
],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"MILVUS_TOKEN": "your-zilliz-cloud-api-key"
}
}
}Core Value
🧠 Your Entire Codebase as Context: Unlike traditional keyword matching that can easily miss relevant code, it uses semantic search to retrieve all your care about from millions of lines of code, without going through multiple hops of discovery, and add to LLM context.
💰 Don’t go bankrupt working with large codebase: Compared to simply stuffing a whole directory of files in LLM context for each call, efficient indexing and search in vector database avoids breaking your budget.
Key Features
🔍 Semantic Code Search: Ask questions like "find functions that handle user authentication" and get relevant, context-rich code instantly.
🧠 Context-Aware: Discover large codebase, understand how different parts of your codebase relate, even across millions of lines of code.
⚡ Incremental Indexing: Efficiently re-index only changed files using Merkle trees.
🧩 Intelligent Code Chunking: Analyze code in Abstract Syntax Trees (AST) for chunking.
🗄️ Scalable: Integrates with Zilliz Cloud for scalable vector search, no matter how large your codebase is.
🛠️ Customizable: Configure file extensions, ignore patterns, and embedding models.
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