Ferret MCP: AI-Powered Knowledge Extraction for Any Codebase

Introduction

Ferret MCP is an innovative Model Context Protocol (MCP) server that revolutionizes how developers understand complex codebases. It's engineered to extract complete knowledge, covering everything from architecture and design patterns to dependencies and API surfaces. By combining robust static analysis with advanced AI interpretation, Ferret MCP provides a deep, expert-level understanding of any repository.

This powerful tool integrates effortlessly with popular MCP clients like Claude Code, Claude Desktop, and Cursor, allowing you to get a senior engineer's analysis of a repository in approximately 30 seconds for a minimal cost.

Why Use Ferret MCP & Key Benefits

Ferret MCP addresses the challenge of quickly grasping unfamiliar or complex codebases, offering a suite of features that enhance developer productivity and understanding:

• Comprehensive Codebase Understanding: Get a holistic view of a project's structure, logic, and underlying principles without manual deep dives.
• Dual Analysis Approach: Benefit from both free, no-LLM-required static analysis tools (e.g., scan, dependencies, architecture, patterns, api_surface, full_extraction) and powerful AI-powered insights.
• AI-Powered Deep Dives: The deep tool generates a 10-section Knowledge Extraction Report, offering an expert analysis that includes an Executive Summary, Architecture Deep Dive, Technology Stack & Rationale, Data & Control Flow, Design Patterns, API & Interface Contracts, Key Files Reading Guide, Strengths, Risks & Technical Debt, and Learning Takeaways.
• Interactive Q&A: Use the ask tool to pose any question about a repository and receive answers grounded in the full codebase context.
• Cost-Effective: AI-powered reports are highly affordable, costing around $0.09 per report when using models like Claude Haiku.
• Flexible LLM Integration: Configure Ferret MCP to work with various LLM providers, including Anthropic, OpenAI-compatible services, and local LLMs like Ollama.

Installation

Getting started with Ferret MCP is straightforward. You can install and run it with uvx without needing to clone the repository, or via pip.

Install & run with uvx (no clone needed)

uvx ferret-mcp

Or install with pip

pip install ferret-mcp

Examples

Ferret MCP is designed to work seamlessly with any MCP client. Here's how to set it up and enable its AI capabilities.

Claude Code Setup

To add Ferret MCP to Claude Code:

claude mcp add ferret -- uvx ferret-mcp

To enable AI-powered tools (deep, ask), set your API key:

claude mcp add ferret -e FERRET_LLM_API_KEY=sk-ant-... -- uvx ferret-mcp

Claude Desktop / Cursor / Any MCP Client

Add the following configuration to your MCP config file (e.g., claude_desktop_config.json, .cursor/mcp.json):

{
  "mcpServers": {
    "ferret": {
      "command": "uvx",
      "args": ["ferret-mcp"],
      "env": {
        "FERRET_LLM_API_KEY": "sk-ant-..."
      }
    }
  }
}

Use with a Local LLM (Ollama)

Ferret MCP can also be configured to use local LLMs, such as those provided by Ollama:

claude mcp add ferret \
  -e FERRET_LLM_PROVIDER=openai \
  -e FERRET_LLM_BASE_URL=http://localhost:11434/v1 \
  -e FERRET_LLM_MODEL=qwen3:8b \
  -- uvx ferret-mcp

Links

• GitHub Repository: fabdendev/ferret-mcp
• PyPI: ferret-mcp