OpenAI MCP Server

MCP Coding Assistant with support for OpenAI + other LLM Providers

A powerful Python recreation of Claude Code with enhanced real-time visualization, cost management, and Model Context Protocol (MCP) server capabilities. This tool provides a natural language interface for software development tasks with support for multiple LLM providers.

Key Features

• Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers
• Model Context Protocol Integration:
  • Run as an MCP server for use with Claude Desktop and other clients
  • Connect to any MCP server with the built-in MCP client
  • Multi-agent synchronization for complex problem solving
• Real-Time Tool Visualization: See tool execution progress and results in real-time
• Cost Management: Track token usage and expenses with budget controls
• Comprehensive Tool Suite: File operations, search, command execution, and more
• Enhanced UI: Rich terminal interface with progress indicators and syntax highlighting
• Context Optimization: Smart conversation compaction and memory management
• Agent Coordination: Specialized agents with different roles can collaborate on tasks

Installation

1. Clone this repository
2. Install dependencies:

3. Create a .env file with your API keys:

Usage

CLI Mode

Run the CLI with the default provider (determined from available API keys):

Specify a provider and model:

Set a budget limit to manage costs:

MCP Server Mode

Run as a Model Context Protocol server:

Start in development mode with the MCP Inspector:

Configure host and port:

Specify additional dependencies:

Load environment variables from file:

MCP Client Mode

Connect to an MCP server using Claude as the reasoning engine:

Specify a Claude model:

Try the included example server:

Multi-Agent MCP Mode

Launch a multi-agent client with synchronized agents:

Use a custom agent configuration file:

Example with the echo server:

Available Tools

• View: Read files with optional line limits
• Edit: Modify files with precise text replacement
• Replace: Create or overwrite files
• GlobTool: Find files by pattern matching
• GrepTool: Search file contents using regex
• LS: List directory contents
• Bash: Execute shell commands

Chat Commands

• /help: Show available commands
• /compact: Compress conversation history to save tokens
• /version: Show version information
• /providers: List available LLM providers
• /cost: Show cost and usage information
• /budget [amount]: Set a budget limit
• /quit, /exit: Exit the application

Architecture

Claude Code Python Edition is built with a modular architecture:

Using with Model Context Protocol

Using Claude Code as an MCP Server

Once the MCP server is running, you can connect to it from Claude Desktop or other MCP-compatible clients:

1. Install and run the MCP server:
   python claude.py serve
2. Open the configuration page in your browser:
   http://localhost:8000
3. Follow the instructions to configure Claude Desktop, including:
   • Copy the JSON configuration
   • Download the auto-configured JSON file
   • Step-by-step setup instructions

Using Claude Code as an MCP Client

To connect to any MCP server using Claude Code:

1. Ensure you have your Anthropic API key in the environment or .env file
2. Start the MCP server you want to connect to
3. Connect using the MCP client:
   python claude.py mcp-client path/to/server.py
4. Type queries in the interactive chat interface

Using Multi-Agent Mode

For complex tasks, the multi-agent mode allows multiple specialized agents to collaborate:

1. Create an agent configuration file or use the provided example
2. Start your MCP server
3. Launch the multi-agent client:
   python claude.py mcp-multi-agent path/to/server.py --config examples/agents_config.json
4. Use the command interface to interact with multiple agents:
   • Type a message to broadcast to all agents
   • Use /talk Agent_Name message for direct communication
   • Use /agents to see all available agents
   • Use /history to view the conversation history

Contributing

1. Fork the repository
2. Create a feature branch
3. Implement your changes with tests
4. Submit a pull request

License

MIT

Acknowledgments

This project is inspired by Anthropic's Claude Code CLI tool, reimplemented in Python with additional features for enhanced visibility, cost management, and MCP server capabilities.# OpenAI Code Assistant

A powerful command-line and API-based coding assistant that uses OpenAI APIs with function calling and streaming.

Features

• Interactive CLI for coding assistance
• Web API for integration with other applications
• Model Context Protocol (MCP) server implementation
• Replication support for high availability
• Tool-based architecture for extensibility
• Reinforcement learning for tool optimization
• Web client for browser-based interaction

Installation

1. Clone the repository
2. Install dependencies:
   pip install -r requirements.txt
3. Set your OpenAI API key:
   export OPENAI_API_KEY=your_api_key

Usage

CLI Mode

Run the assistant in interactive CLI mode:

Options:

• --model, -m: Specify the model to use (default: gpt-4o)
• --temperature, -t: Set temperature for response generation (default: 0)
• --verbose, -v: Enable verbose output with additional information
• --enable-rl/--disable-rl: Enable/disable reinforcement learning for tool optimization
• --rl-update: Manually trigger an update of the RL model

API Server Mode

Run the assistant as an API server:

Options:

• --host: Host address to bind to (default: 127.0.0.1)
• --port, -p: Port to listen on (default: 8000)
• --workers, -w: Number of worker processes (default: 1)
• --enable-replication: Enable replication across instances
• --primary/--secondary: Whether this is a primary or secondary instance
• --peer: Peer instances to replicate with (host), can be specified multiple times

MCP Server Mode

Run the assistant as a Model Context Protocol (MCP) server:

Options:

• --host: Host address to bind to (default: 127.0.0.1)
• --port, -p: Port to listen on (default: 8000)
• --dev: Enable development mode with additional logging
• --dependencies: Additional Python dependencies to install
• --env-file: Path to .env file with environment variables

MCP Client Mode

Connect to an MCP server using the assistant as the reasoning engine:

Options:

• --model, -m: Model to use for reasoning (default: gpt-4o)
• --host: Host address for the MCP server (default: 127.0.0.1)
• --port, -p: Port for the MCP server (default: 8000)

Deployment Script

For easier deployment, use the provided script:

To enable replication:

Web Client

To use the web client, open web-client.html in your browser. Make sure the API server is running.

API Endpoints

Standard API Endpoints

• POST /conversation: Create a new conversation
• POST /conversation/{conversation_id}/message: Send a message to a conversation
• POST /conversation/{conversation_id}/message/stream: Stream a message response
• GET /conversation/{conversation_id}: Get conversation details
• DELETE /conversation/{conversation_id}: Delete a conversation
• GET /health: Health check endpoint

MCP Protocol Endpoints

• GET /: Health check (MCP protocol)
• POST /context: Get context for a prompt template
• GET /prompts: List available prompt templates
• GET /prompts/{prompt_id}: Get a specific prompt template
• POST /prompts: Create a new prompt template
• PUT /prompts/{prompt_id}: Update an existing prompt template
• DELETE /prompts/{prompt_id}: Delete a prompt template

Replication

The replication system allows running multiple instances of the assistant with synchronized state. This provides:

• High availability
• Load balancing
• Fault tolerance

To set up replication:

1. Start a primary instance with --enable-replication
2. Start secondary instances with --enable-replication --secondary --peer [primary-host:port]

Tools

The assistant includes various tools:

• Weather: Get current weather for a location
• View: Read files from the filesystem
• Edit: Edit files
• Replace: Write files
• Bash: Execute bash commands
• GlobTool: File pattern matching
• GrepTool: Content search
• LS: List directory contents
• JinaSearch: Web search using Jina.ai
• JinaFactCheck: Fact checking using Jina.ai
• JinaReadURL: Read and summarize webpages

CLI Commands

• /help: Show help message
• /compact: Compact the conversation to reduce token usage
• /status: Show token usage and session information
• /config: Show current configuration settings
• /rl-status: Show RL tool optimizer status (if enabled)
• /rl-update: Update the RL model manually (if enabled)
• /rl-stats: Show tool usage statistics (if enabled)