How to Supercharge AI Coding Agents with Semantic Code Search: A Complete Guide to Claude Context

Introduction

Claude Context is a powerful Model Context Protocol (MCP) plugin that brings semantic code search to Claude Code, Gemini CLI, and other AI coding agents. By leveraging advanced vector search and embeddings, Claude Context enables your AI assistant to access and understand your entire codebase, making code search and context retrieval smarter, faster, and more cost-effective.

Whether you’re working with millions of lines of code or just starting a new project, Claude Context helps AI agents answer complex code questions, find relevant snippets, and provide deep context—all without breaking your LLM budget.

Core Value

• Your Entire Codebase as Context: Uses semantic search to retrieve relevant code from massive codebases, not just keyword matches.
• Cost-Efficient: Efficient indexing and vector search avoid the high costs of stuffing entire directories into LLM context.

Demo

See Claude Context in action:

Step-by-Step Tutorial: Setting Up Claude Context

1. Prerequisites

• Zilliz Cloud Account: Sign up for Zilliz Cloud to get a free vector database and API key.
• OpenAI API Key: Get your OpenAI API key for embedding models.

2. Configure MCP for Your AI Assistant

Claude Context integrates with many AI coding agents. Here’s how to set it up for the most popular ones:

Claude Code

# Add the Claude Context MCP server
claude mcp add claude-context -e OPENAI_API_KEY=your-openai-api-key -e MILVUS_TOKEN=your-zilliz-cloud-api-key -- npx @zilliz/claude-context-mcp@latest

See the Claude Code MCP documentation for more details.

Gemini CLI

{
  "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"
      }
    }
  }
}

Edit ~/.gemini/settings.json and restart Gemini CLI.

Other Clients (Cursor, Void, VSCode, etc.)

Most clients use a similar JSON configuration. Example for Cursor:

{
  "mcpServers": {
    "claude-context": {
      "command": "npx",
      "args": ["-y", "@zilliz/claude-context-mcp@latest"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key",
        "MILVUS_ADDRESS": "your-zilliz-cloud-public-endpoint",
        "MILVUS_TOKEN": "your-zilliz-cloud-api-key"
      }
    }
  }
}

For more detailed configuration, see the Environment Variables Guide.

3. Using the Core Package Directly

Developers can use the @zilliz/claude-context-core package for custom integrations:

import { Context, MilvusVectorDatabase, OpenAIEmbedding } from '@zilliz/claude-context-core';

// Initialize embedding provider
const embedding = new OpenAIEmbedding({
    apiKey: process.env.OPENAI_API_KEY || 'your-openai-api-key',
    model: 'text-embedding-3-small'
});

// Initialize vector database
const vectorDatabase = new MilvusVectorDatabase({
    address: process.env.MILVUS_ADDRESS || 'your-zilliz-cloud-public-endpoint',
    token: process.env.MILVUS_TOKEN || 'your-zilliz-cloud-api-key'
});

// Create context instance
const context = new Context({
    embedding,
    vectorDatabase
});

// Index your codebase with progress tracking
const stats = await context.indexCodebase('./your-project', (progress) => {
    console.log(`${progress.phase} - ${progress.percentage}%`);
});
console.log(`Indexed ${stats.indexedFiles} files, ${stats.totalChunks} chunks`);

// Perform semantic search
const results = await context.semanticSearch('./your-project', 'vector database operations', 5);
results.forEach(result => {
    console.log(`File: ${result.relativePath}:${result.startLine}-${result.endLine}`);
    console.log(`Score: ${(result.score * 100).toFixed(2)}%`);
    console.log(`Content: ${result.content.substring(0, 100)}...`);
});

4. VSCode Extension

Install the Semantic Code Search extension for Visual Studio Code for an intuitive, in-editor experience.

Key Features

• Semantic Code Search: Find relevant code by meaning, not just keywords.
• Context-Aware: Understands relationships across large codebases.
• Incremental Indexing: Only re-indexes changed files for efficiency.
• Intelligent Code Chunking: Uses ASTs for smarter code segmentation.
• Scalable: Handles millions of lines of code with Zilliz Cloud.
• Customizable: Supports multiple embedding providers, file types, and ignore patterns.

Supported Technologies

• Embedding Providers: OpenAI, VoyageAI, Ollama, Gemini
• Vector Databases: Milvus, Zilliz Cloud
• Languages: TypeScript, JavaScript, Python, Java, C++, C#, Go, Rust, PHP, Ruby, Swift, Kotlin, Scala, Markdown, and more
• Development Tools: VSCode, Model Context Protocol

Conclusion

Claude Context is a game-changer for AI-powered code search and context retrieval. By integrating semantic search and scalable vector databases, it empowers AI coding agents to deliver smarter, more relevant results—no matter the size of your codebase.

For more expert insights and tutorials on AI and automation, visit us at decisioncrafters.com.