What is Treesitter Mcp

treesitter-mcp is a high-precision, multi-language code search and analysis server designed for AI agents and developers, built on the Model Context Protocol (MCP). It utilizes Tree-sitter to parse code into concrete syntax trees, allowing for structural and syntax-aware queries.

Use cases

Use cases for treesitter-mcp include finding specific code patterns in large codebases, generating documentation from code structure, assisting in code reviews, and enhancing AI agents’ understanding of programming languages.

How to use

To use treesitter-mcp, set up the server in your development environment and utilize its API to perform code searches and analyses. You can query for specific code patterns, extract contextual snippets, and analyze code structure across multiple languages.

Key features

Key features include structural search capabilities, multi-language support (TypeScript, JavaScript, Python, Go, and Java), high-level analysis of code structure, contextual snippet extraction, a clean and modular architecture, and token-aware middleware for logging.

Where to use

treesitter-mcp can be used in software development environments, code analysis tools, AI-driven code assistants, and any application requiring high-precision code search and analysis across multiple programming languages.

🌳 Tree-sitter Code Search MCP Server

A high-precision, multi-language code search and analysis server for AI agents and developers, built on the Model Context Protocol (MCP).

This server leverages the power of Tree-sitter to parse code into concrete syntax trees, enabling structural, syntax-aware queries that are impossible with traditional text-based search. This means zero false positives and a deep, semantic understanding of your codebase.

💡 Why Use This Server?

✨ Features

• 🧠 Structural Search: Find code patterns based on syntax, not just text.
• 🌐 Multi-Language Support: Ships with support for TypeScript, JavaScript, Python, Go, and Java out of the box.
• 🗺️ High-Level Analysis: Quickly list all functions, classes, or imports in a file to map out its structure.
• ✂️ Contextual Snippets: Surgically extract the full body of a function or class for focused analysis.
• 🏗️ Clean & Modular: Built with a clean architecture for easy extension and maintenance.
• 📊 Token-Aware: Includes middleware to count and log token usage for all tool responses.

🛠️ Available Tools

This server provides a powerful suite of tools for code analysis.

For detailed technical guidelines, see .clinerules/treesitter-server-guidelines.md.

✨ What’s New (as of d957324)

• Integration Testing Guidelines: Added comprehensive guidelines for writing integration tests for new tools. This ensures that all new features are tested robustly and consistently. For more details, see .clinerules/integration-testing-guidelines.md.

🚀 Getting Started

1. Installation

Clone the repository and install the dependencies using pnpm.

git clone <repository-url>
cd tree-sitter-mcp
pnpm install

2. Build

Compile the TypeScript source code.

pnpm run build

The compiled output will be in the build/ directory.

⚙️ Setup for Use

To use this server with an MCP client like Cline, you need to add it to your client’s configuration file.

1. Locate your configuration file. For Cline, this is typically found at:

   • macOS: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

2. Add the server configuration: Open the JSON file and add the following entry to the mcpServers object. Make sure to replace <path-to-this-project> with the absolute path to this project’s directory on your machine.

{
  "mcpServers": {
    "... other servers": {},
    "treesitter-code-search": {
      "command": "node",
      "args": [
        "<path-to-this-project>/tree-sitter-mcp/build/index.js"
      ],
      "disabled": false,
      "autoApprove": [
        "initialize_treesitter_context",
        "structural_code_search",
        "list_code_elements_by_kind",
        "get_contextual_code_snippets"
      ]
    }
  }
}

3. Restart your MCP client. The Tree-sitter server should now be available.

🤖 Prompt for AI Agents

To ensure a coding agent effectively uses this server for code analysis tasks, you can use a system prompt or initial instruction like this:

System Prompt:

You have access to a powerful treesitter-code-search MCP server for code analysis. To use it effectively, you must follow this workflow:

1. Initialize: Begin every session by calling initialize_treesitter_context with the languages in the project (e.g., ['typescript', 'python']).
2. Analyze: Call any analysis tool (structural_code_search, list_code_elements_by_kind, etc.) with the absolute path to a file. The server will automatically parse the file on the first request. There is no need to call a separate parsing tool.
3. Explore: Use list_code_elements_by_kind to get a high-level overview of a file’s structure (e.g., list all 'function_declaration').
4. Analyze: Use get_contextual_code_snippets to view the full implementation of a specific function or class you’ve identified.
5. Search: Use structural_code_search with a Tree-sitter query for precise, syntax-aware searches.

Always prefer these structural analysis tools over simple text search for higher accuracy and zero false positives.

Happy coding!