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MCP Claude - Document Management CLI

A powerful command-line interface application that integrates Model Context Protocol (MCP) with Claude AI for intelligent document management, formatting, and summarization.

🎯 Overview

MCP Claude is a Python-based CLI tool that demonstrates how to build AI-powered document workflows using the Model Context Protocol. It provides a conversational interface where users can interact with documents, apply AI-driven formatting, and generate summaries—all through natural language commands.

✨ Features

MCP Server Capabilities

CLI Features

• 💬 Interactive Chat - Natural language conversation with Claude AI
• 📄 Document Mentions - Reference documents using @document.ext syntax
• 🔧 Tool Integration - AI automatically uses available tools to complete tasks
• 🔄 Multi-Client Support - Connect multiple MCP servers simultaneously

🏗️ Architecture

mcp_claude_1/
├── main.py              # Application entry point
├── mcp_server.py        # MCP server with tools, resources, and prompts
├── mcp_client.py        # MCP client wrapper for server communication
├── core/
│   ├── chat.py          # Base chat functionality
│   ├── cli_chat.py      # CLI-specific chat implementation
│   ├── cli.py           # CLI application UI
│   ├── claude.py        # Anthropic Claude API wrapper
│   └── tools.py         # Tool execution manager
├── pyproject.toml       # Project configuration
└── .env                 # Environment variables (API keys)

🚀 Getting Started

Prerequisites

• Python 3.10+
• Anthropic API key
• uv (recommended) or pip

🟢 Option 1: Using uv (Recommended)

uv is an extremely fast Python package and project manager written in Rust. It handles virtual environments and dependencies automatically, making setup straightforward.

Step 1: Install uv

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or via Homebrew (macOS)
brew install uv

Step 2: Clone the Repository

git clone https://github.com/lambdamax/mcp_claude_1.git
cd mcp_claude_1

Step 3: Install Dependencies

# uv automatically creates .venv and installs all dependencies
uv sync

Note: uv sync reads pyproject.toml and uv.lock to install exact dependency versions, ensuring reproducible builds.

Step 4: Configure Environment Variables

Create a .env file in the project root:

CLAUDE_MODEL="claude-sonnet-4-5"
ANTHROPIC_API_KEY="<YOUR-CLAUDE-API-KEY>"

# Set to 1 when using uv
USE_UV=1

Step 5: Run the Application

# uv run automatically uses the project's virtual environment
uv run python main.py

Why use uv?

• ⚡ 10-100x faster than pip
• 🔒 Lock file support (uv.lock) for reproducible builds
• 🎯 Automatic virtual environment management
• 📦 Built-in dependency resolver

🔵 Option 2: Using pip (Alternative)

Traditional Python setup using pip and venv.

Step 1: Clone the Repository

git clone https://github.com/lambdamax/mcp_claude_1.git
cd mcp_claude_1

Step 2: Create Virtual Environment

python3 -m venv .venv

Step 3: Activate Virtual Environment

# macOS / Linux
source .venv/bin/activate

# Windows (Command Prompt)
.venv\Scripts\activate

# Windows (PowerShell)
.venv\Scripts\Activate.ps1

Step 4: Install Dependencies

pip install -r requirements.txt

# Or install in editable mode for development
pip install -e .

Step 5: Configure Environment Variables

Create a .env file in the project root:

CLAUDE_MODEL="claude-sonnet-4-5"
ANTHROPIC_API_KEY="<YOUR-CLAUDE-API-KEY>"

# Set to 0 when using pip
USE_UV=0

Step 6: Run the Application

# Make sure virtual environment is activated
source .venv/bin/activate  # macOS/Linux

# Run the application
python main.py

📖 Usage

Interactive Commands

Once the CLI is running, you can interact with documents:

> What documents are available?
> @report.pdf What does this report cover?
> /format deposition.md
> /summarize financials.docx

Document Mentions

Reference documents directly in your queries using the @ prefix:

> Compare @report.pdf with @outlook.pdf
> Summarize the key points from @deposition.md

Prompt Commands

Use slash commands to trigger specific AI workflows:

🔧 MCP Server Details

Tools

read_doc_content

Reads and returns the content of a document.

# Input
{"doc_id": "report.pdf"}

# Output
"The report details the state of a 20m condenser tower."

edit_doc_content

Edits document content by replacing strings.

# Input
{
    "doc_id": "report.pdf",
    "old_strings": ["20m"],
    "new_strings": ["25m"]
}

Resources

• docs://documents - Returns a JSON array of all document IDs
• docs://documents/{doc_id} - Returns the plain text content of a specific document

Prompts

The MCP server exposes prompts that provide structured instructions to the AI:

• format - Guides the AI to reformat documents into clean Markdown with proper headers, lists, and tables
• summarize - Instructs the AI to extract key points and create concise summaries

🧪 Testing with MCP Inspector

Test your MCP server implementation using the MCP Inspector:

# Using uv (recommended)
npx @modelcontextprotocol/inspector uv run python mcp_server.py

# Using pip/venv (make sure .venv is activated)
npx @modelcontextprotocol/inspector .venv/bin/python mcp_server.py

This opens a web interface at http://localhost:6274 where you can:

• View available tools, resources, and prompts
• Test tool calls with different inputs
• Debug server responses

📦 Dependencies

🔐 Environment Variables

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is open source and available under the MIT License.

🔗 Resources

• Model Context Protocol Documentation
• Anthropic Claude API
• FastMCP Framework
• uv - Python Package Manager