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MCP server · Communication

Berry MCP Anthropic

A version of berry-mcp but uses the Anthropic sdk

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Scored 6 months ago · breakdown

About Berry MCP Anthropic

Berry MCP Anthropic is an MCP server in the Communication category: a version of berry-mcp but uses the Anthropic sdk.

Install

From source
git clone https://github.com/richinex/berry-mcp-anthropic

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README

Berry MCP Anthropic

CI Tests uv

A universal Model Context Protocol (MCP) server framework built on Anthropic's official MCP SDK with FastMCP. Makes it easy to create and deploy custom tool servers for AI assistants like Claude.

✨ Features

  • 🏢 Official MCP SDK: Built on Anthropic's official MCP SDK with FastMCP
  • 🔧 Universal Framework: Create MCP servers for any type of tools
  • 🎯 Simple Tool Creation: Decorator-based tool registration with automatic JSON schema generation
  • 🔌 Plugin Architecture: Load tools from any Python module or package
  • 🚀 Multiple Transports: Support for stdio transport (FastMCP managed)
  • ⚙️ Flexible Configuration: Environment variables and command-line options
  • 📝 Auto-Documentation: Automatic tool discovery and schema generation
  • 🔒 Type Safety: Full type annotation support with validation
  • 📄 PDF Processing: Built-in PDF text extraction tools with PyMuPDF and PyPDF2

🚀 Quick Start

Installation

# Install from PyPI (when published)
uv add berry-mcp-anthropic

# Or install from source
git clone https://github.com/richinex/berry-mcp-anthropic.git
cd berry-mcp-anthropic
uv pip install -e .

Create Your First Tool

# my_tools.py
from berry_mcp.tools.decorators import tool

@tool(description="Add two numbers together")
def add_numbers(a: float, b: float) -> float:
    """Add two numbers and return the result"""
    return a + b

@tool(description="Generate a greeting message")  
def greet(name: str, title: str = "friend") -> str:
    """Generate a personalized greeting"""
    return f"Hello {title} {name}!"

@tool(description="Extract text from PDF files")
async def process_pdf(file_path: str) -> str:
    """Process PDF and extract text content"""
    # Built-in PDF tools are available automatically
    from berry_mcp.tools.pdf_tools import read_pdf_text
    return await read_pdf_text(file_path)

Run Your Server

# Load your custom tools
BERRY_MCP_TOOLS_PATH=my_tools uv run python -m berry_mcp

# Or run with built-in tools (includes PDF processing)
uv run python -m berry_mcp

# Test the server is working
echo '{"jsonrpc": "2.0", "id": 1, "method": "initialize", "params": {"protocolVersion": "2024-11-05", "capabilities": {}, "clientInfo": {"name": "test", "version": "1.0"}}}' | uv run python -m berry_mcp

VS Code Integration

Add to your .vscode/mcp.json:

{
  "inputs": [],
  "servers": {
    "my-custom-tools": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "python", "-m", "berry_mcp"],
      "env": {
        "BERRY_MCP_TOOLS_PATH": "my_tools"
      }
    }
  }
}

📖 Documentation

🛠️ Built-in Tools

Berry MCP Anthropic comes with comprehensive tools to get you started:

  • PDF Processing: read_pdf_text, read_pdf_text_pypdf2 - Extract text from PDF files
  • Math Operations: add_numbers, generate_random
  • Text Processing: format_text, find_replace_text, encode_decode_text
  • System Info: get_system_info, generate_uuid
  • Data Tools: validate_json, generate_report
  • Async Examples: async_process_text

All tools are automatically discovered and registered with the FastMCP framework.

🔧 Advanced Usage

Multiple Tool Sources

BERRY_MCP_TOOLS_PATH="my_tools,web_tools,data_processors" uv run python -m berry_mcp

Custom Server Configuration

# Custom server name and logging
BERRY_MCP_SERVER_NAME="my-pdf-server" BERRY_MCP_LOG_LEVEL="DEBUG" uv run python -m berry_mcp

Environment Configuration

export BERRY_MCP_SERVER_NAME="my-custom-server"
export BERRY_MCP_LOG_LEVEL="DEBUG"
export BERRY_MCP_TOOLS_PATH="my_tools,another_module.tools"
uv run python -m berry_mcp

🏗️ Architecture

Berry MCP Anthropic follows SOLID principles with a clean, extensible architecture built on FastMCP:

  • FastMCP Integration: Built on Anthropic's official MCP SDK
  • Tool Registry: Global tool registration with FastMCP integration
  • Auto-Discovery: Automatic tool loading from modules
  • Decorator Framework: Simple @tool decorator for registration
  • Transport Layer: FastMCP-managed stdio communication
  • Type Safety: Full mypy support with proper type annotations

📋 Requirements

  • Python 3.10+
  • Anthropic's MCP SDK (FastMCP)
  • Type annotations for automatic schema generation
  • Optional: PyMuPDF and PyPDF2 for PDF processing

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Make your changes following the existing patterns
  4. Add tests for new functionality
  5. Run the test suite: uv run pytest tests/
  6. Check code quality: uv run ruff check src/ && uv run mypy src/
  7. Submit a pull request

📝 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

  • Built on the Model Context Protocol
  • Inspired by the need for easy MCP server creation
  • Following clean code principles and design patterns

🚀 Start building your custom MCP tools today with Berry MCP Server!

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Berry MCP Anthropic

How do I install Berry MCP Anthropic?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Berry MCP Anthropic safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Berry MCP Anthropic still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.