A2A + MCP = Next-Level Agent Communication
AI Summary
Summary of Building AI Agents with A2A and MCP
Overview
- Introduction to building autonomous AI agents using two key open-source protocols: A2A (Agent-to-Agent) and MCP (Model Context Protocol).
Why Standards Are Needed
- Current custom communication methods hinder the scalability and reliability of AI systems.
Agent-to-Agent Protocol (A2A)
- Agent Card: A JSON file located at
/wellknown/agent.json
- Describes agent capabilities, API endpoints, and authentication.
- A2A Server: Acts as the agent handling incoming tasks via a primary endpoint.
- A2A Client: Can be another agent or application, sends tasks to the server, and receives updates or results.
Model Context Protocol (MCP)
- Tools: Executable actions provided by the MCP server (e.g., fetching weather data).
- Resources: Read-only data sources that provide context.
- Transport: MCP supports communication over HTTP, WebSockets, or local sockets.
Complementarity of A2A and MCP
- A2A facilitates agent communication, while MCP standardizes access to external tools and data.
Typical Workflow Using A2A
- Discovery: Fetches agent card from a known URL.
- Initiation: Client sends the initial request with a unique task ID.
- Processing: Includes streaming updates or direct responses.
- Interaction: Allows additional client input if needed.
- Completion: Task reaches a final state (completed, failed, or canceled).
Practical Examples
- Implementation details using VS Code to set up both protocols.
- Code snippets demonstrating how to create A2A servers and clients that communicate effectively, including fetching data from external APIs.
Conclusion
- Questions can be asked in the comments. Videos can help understand concepts better.