MCP Server

A Model Context Protocol (MCP) server implementation that provides document management capabilities through a CLI interface powered by Claude AI.

Overview

This project implements an MCP server that allows you to:

• Read and edit documents

• List available documents

• Format documents using Markdown

• Summarize document contents

• Interact with documents through a CLI chat interface powered by Claude AI

Features

• Document Management: Read, edit, and manage various document types

• MCP Protocol: Implements the Model Context Protocol for AI tool integration

• Claude AI Integration: Uses Anthropic's Claude for intelligent document processing

• CLI Interface: Command-line chat interface for document interactions

• Multiple Document Formats: Support for various document types (MD, PDF, DOCX, TXT)

Prerequisites

• Python 3.8+

• UV package manager (recommended) or pip

• Anthropic API key

Installation

1. Clone the repository:

git clone <repository-url>
cd mcp-server

2. Create and activate a virtual environment:

# Using UV (recommended)
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Or using standard Python
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

# Using UV
uv pip install -r requirements.txt

# Or using pip
pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the project root:

CLAUDE_MODEL=claude-3-sonnet-20240229
ANTHROPIC_API_KEY=your_anthropic_api_key_here
USE_UV=1  # Set to 0 if not using UV

Usage

Basic Usage

Run the main application:

python main.py

Or with UV:

uv run main.py

Running the MCP Server

The MCP server can be run independently:

python mcp_server.py

Or with UV:

uv run mcp_server.py

CLI Chat Interface

The application provides a CLI chat interface where you can:

• Ask questions about documents

• Request document summaries

• Format documents

• Edit document contents

Project Structure

mcp-server/
├── core/                   # Core application modules
│   ├── chat.py            # Chat functionality
│   ├── claude.py          # Claude AI service integration
│   ├── cli.py             # CLI application logic
│   ├── cli_chat.py        # CLI chat interface
│   └── tools.py           # Utility tools
├── mcp_server.py          # MCP server implementation
├── mcp_client.py          # MCP client implementation
├── main.py                # Main application entry point
└── README.md              # This file

MCP Server Features

The MCP server provides the following tools and resources:

Tools

• read_document: Read contents of a document

• edit_document: Edit document content by replacing text

Resources

• docs://documents: List all available documents

• docs://documents/{doc_id}: Get specific document content

Prompts

• format: Rewrite document in Markdown format

• summarize: Summarize document contents

Available Documents

The server comes with sample documents:

• deposition.md - Testimony documentation

• report.pdf - Technical report

• financials.docx - Financial information

• outlook.pdf - Performance projections

• plan.md - Implementation plan

• spec.txt - Technical specifications

Configuration

Environment Variables

• CLAUDE_MODEL: The Claude model to use (default: claude-3-sonnet-20240229)

• ANTHROPIC_API_KEY: Your Anthropic API key

• USE_UV: Set to "1" to use UV, "0" to use standard Python

UV vs Python

The application can use either UV or standard Python. Set USE_UV=1 in your .env file to use UV, or USE_UV=0 to use standard Python.

Development

Running Tests

# Using UV
uv run pytest

# Using Python
python -m pytest

Code Style

The project follows Python best practices and uses type hints throughout.

Troubleshooting

Common Issues

1. API Key Error: Ensure your ANTHROPIC_API_KEY is set correctly in the .env file

2. Model Error: Verify the CLAUDE_MODEL is a valid model name

3. UV Not Found: Install UV or set USE_UV=0 to use standard Python

Debug Mode

The MCP server runs in DEBUG mode by default. Check the console output for detailed logging information.

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests if applicable

5. Submit a pull request

License

[Add your license information here]

Support

For issues and questions, please open an issue on the repository.