The MCP Agent implements a ReWOO pattern-based framework, enabling efficient interaction with MCP servers, reducing token use, and optimizing reasoning processes for language models of varying sizes.
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Created by:
Apr 28 2025
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What is mcp-agent?
This project develops a LangGraph-based agent that reasons and acts through a series of nodes such as tool filtering, plan generation, validation, and execution, to efficiently utilize MCP servers. It employs the ReWOO pattern to predefine evidence and reduce prompt complexity. The system supports multiple LLM providers, supports dynamic plan correction, and features modules for tool filtering, planning, validation, and evidence processing, ensuring minimal token consumption and optimized performance across diverse reasoning tasks.
Who will use mcp-agent?
- AI Researchers
- Developers utilizing MCP protocols
- Organizations integrating MCP-driven automation
How to use the mcp-agent?
- Step1: Clone the repository from GitHub
- Step2: Set up a virtual environment and install dependencies
- Step3: Configure environment variables and MCP server settings
- Step4: Run the main.py script to start the agent
- Step5: Interact via the /chat endpoint to send messages and receive responses
mcp-agent's Core Features & Benefits
The Core Features
- Tool filtering
- Plan generation and validation
- Tool execution
- Evidence processing
- Final answer synthesis
The Benefits
- Efficient MCP utilization
- Reduced token consumption
- Dynamic plan correction
- Compatibility with multiple LLM providers
- Optimized reasoning workflow
mcp-agent's Main Use Cases & Applications
- Automated reasoning and decision-making
- MCP server resource management
- Intelligent agent workflow automation