Oak MCP

Oak MCP is a Model Context Protocol (MCP) server implementation for the Oak framework, enabling AI models to interact directly with Oak's deep semantic analysis.

🎯 Overview

Oak MCP bridges the gap between AI agents (like Claude 3.5/4) and your source code. Unlike traditional LSP-based tools that return verbose JSON, Oak MCP is optimized for AI context windows, providing structured "code skeletons" and intelligent semantic search capabilities.

✨ Features

• Compact Format: Deeply optimized for AI context windows. Returns "code skeletons" using Oak's RedTree architecture to minimize token usage.
• Fuzzy Semantics: Integrated with oak-semantic-search for natural language intent-based code retrieval.
• Local Embedding: Uses fastembed for privacy-first, offline vector indexing.
• LSP Bridge: Supports standard features like Hover, Definition, References, and Symbols.
• Cross-File Context: Discovers logical relationships between files that go beyond simple naming conventions.

🚀 Quick Start

Add oak-mcp to your Cargo.toml:

[dependencies]
oak-mcp = "0.0.1"

Running the MCP Server

use oak_mcp::OakMcpService;
use oak_rust::RustService; // Example language service

#[tokio::main]
async fn main() {
    let service = RustService::new();
    let server = service.into_mcp_server()
        .with_searcher(semantic_searcher);
    
    server.run().await.unwrap();
}

📋 Examples

Integration with Claude Desktop

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "oak": {
      "command": "path/to/your/oak-mcp-binary",
      "args": []
    }
  }
}

🏗️ Advanced Capabilities

1. Structured Summarization

Instead of sending thousands of lines of code, Oak MCP sends a hierarchical summary of symbols and their relationships, allowing the AI to understand the big picture instantly.

2. Intent-Based Search

"How does this project handle error retries?" — Oak MCP uses vector embeddings to find relevant code chunks even if the word "retry" isn't explicitly in the function name.

📊 Performance

• Token Efficiency: Up to 80% reduction in token usage compared to raw file contents.
• Low Latency: Local indexing and fast retrieval for real-time AI interactions.

🤝 Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Oak MCP - Deep code understanding for AI agents 🚀