MCP Coding Assistant
by arthurcolle
A powerful coding assistant with real-time visualization, cost management, and Model Context Protocol (MCP) server capabilities. It provides a natural language interface for software development tasks with support for multiple LLM providers.
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What is MCP Coding Assistant?
The MCP Coding Assistant is a Python-based tool that enhances software development by providing a natural language interface powered by multiple Large Language Models (LLMs). It supports OpenAI, Anthropic, and other providers, offering features like real-time tool visualization, cost management, and Model Context Protocol (MCP) integration for seamless interaction with Claude Desktop and other clients.
How to use MCP Coding Assistant?
To use the assistant, first clone the repository and install the required dependencies using pip install -r requirements.txt. Configure your API keys in a .env file. You can then run the CLI in chat mode (python claude.py chat), MCP server mode (python claude.py serve), or MCP client mode (python claude.py mcp-client path/to/server.py). The multi-agent mode (python claude.py mcp-multi-agent path/to/server.py) allows for collaborative problem-solving with specialized agents.
Key features of MCP Coding Assistant
Multi-Provider Support (OpenAI, Anthropic, etc.)
Model Context Protocol (MCP) Integration (Server and Client)
Real-Time Tool Visualization
Cost Management (Token Usage Tracking)
Comprehensive Tool Suite (File Operations, Search, Command Execution)
Enhanced UI (Rich Terminal Interface)
Context Optimization (Conversation Compaction)
Agent Coordination (Multi-Agent Collaboration)
Use cases of MCP Coding Assistant
Generating code snippets from natural language descriptions
Debugging and troubleshooting code
Automating repetitive coding tasks
Integrating with existing development workflows
Building complex software systems with collaborative agents
Creating and managing prompt templates
Fact checking using Jina.ai
Reading and summarizing webpages
FAQ from MCP Coding Assistant
What LLM providers are supported?
What LLM providers are supported?
The assistant supports OpenAI, Anthropic, and other LLM providers.
How do I manage costs?
How do I manage costs?
You can track token usage and expenses with budget controls using the /budget command.
What is MCP and how is it used?
What is MCP and how is it used?
Model Context Protocol (MCP) allows the assistant to function as a server for Claude Desktop and other clients, enabling seamless integration and communication.
How do I run the assistant in multi-agent mode?
How do I run the assistant in multi-agent mode?
Launch the multi-agent client using python claude.py mcp-multi-agent path/to/server.py --config examples/agents_config.json after starting an MCP server and configuring an agent configuration file.
Can I extend the assistant with custom tools?
Can I extend the assistant with custom tools?
Yes, the tool-based architecture allows for extensibility with custom tools. You can implement new tools and integrate them into the assistant's workflow.