A2A MCP Server
About
A bridge server connecting Model Context Protocol (MCP) with Agent-to-Agent (A2A) protocol.
Details
- Author
- yw0nam
- Categories
- Communication, AI, Automation
Jump to
Setup
Install A2A MCP Server in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/yw0nam/mcp_a2a_gateway
Follow the installation instructions in the repository README, then restart your MCP client.
A bridge server connecting Model Context Protocol (MCP) with Agent-to-Agent (A2A) protocol.
A gateway server that bridges the Model Context Protocol (MCP) with the Agent-to-Agent (A2A) protocol, enabling MCP-compatible AI assistants (like Claude) to seamlessly interact with A2A agents.
This project serves as an integration layer between two cutting-edge AI agent protocols:
-
Model Context Protocol (MCP): Developed by Anthropic, MCP allows AI assistants to connect to external tools and data sources. It standardizes how AI applications and large language models connect to external resources in a secure, composable way.
Agent-to-Agent Protocol (A2A): Developed by Google, A2A enables communication and interoperability between different AI agents through a standardized JSON-RPC interface.
By bridging these protocols, this server allows MCP clients (like Claude) to discover, register, communicate with, and manage tasks on A2A agents through a unified interface.
# Run with default settings (stdio transport) uvx mcp-a2a-gateway # Run with HTTP transport for web clients MCP_TRANSPORT=streamable-http MCP_PORT=10000 uvx mcp-a2a-gateway # Run with custom data directory MCP_DATA_DIR="/Users/your-username/Desktop/a2a_data" uvx mcp-a2a-gateway # Run with specific version uvx mcp-a2a-gateway==0.1.6 # Run with multiple environment variables MCP_TRANSPORT=stdio MCP_DATA_DIR="/custom/path" LOG_LEVEL=DEBUG uvx mcp-a2a-gateway
# Clone and run locally git clone https://github.com/yw0nam/MCP-A2A-Gateway.git cd MCP-A2A-Gateway # Run with uv uv run mcp-a2a-gateway # Run with uvx from local directory uvx --from . mcp-a2a-gateway # Run with custom environment for development MCP_TRANSPORT=streamable-http MCP_PORT=8080 uvx --from . mcp-a2a-gateway
1, Run The hello world Agent in A2A Sample
2, Use Claude or github copilot to register the agent.
3, Use Claude to Send a task to the hello Agent and get the result.
4, Use Claude to retrieve the task result.
- Register A2A agents with the bridge server
- List all registered agents
- Unregister agents when no longer needed
- Send messages to A2A agents and receive responses
- Asynchronous message sending for immediate server response.
- Stream responses from A2A agents in real-time
- Track which A2A agent handles which task
- Retrieve task results using task IDs
- Get a list of all tasks and their statuses.
- Cancel running tasks
- Multiple transport types: stdio, streamable-http, SSE
- Configure transport type using MCP_TRANSPORT environment variable
Before you begin, ensure you have the following installed:
Run directly without installation usinguvx:
git clone https://github.com/yw0nam/MCP-A2A-Gateway.git cd MCP-A2A-Gateway
# Using uvx MCP_TRANSPORT=streamable-http MCP_HOST=0.0.0.0 MCP_PORT=10000 uvx mcp-a2a-gateway
# Using uvx MCP_TRANSPORT=sse MCP_HOST=0.0.0.0 MCP_PORT=10000 uvx mcp-a2a-gateway
The server can be configured using the following environment variables:
# Transport configuration MCP_TRANSPORT=stdio MCP_HOST=0.0.0.0 MCP_PORT=10000 MCP_PATH=/mcp # Data storage MCP_DATA_DIR=/Users/your-username/Desktop/data/a2a_gateway # Timeouts MCP_REQUEST_TIMEOUT=30 MCP_REQUEST_IMMEDIATE_TIMEOUT=2 # Logging LOG_LEVEL=INFO
The A2A MCP Server supports multiple transport types:
-
stdio(default): Uses standard input/output for communication
- Ideal for command-line usage and testing
- No HTTP server is started
- Required for Claude Desktop
streamable-http(recommended for web clients): HTTP transport with streaming support
- Recommended for production deployments
- Starts an HTTP server to handle MCP requests
- Enables streaming of large responses
- Provides real-time event streaming
- Useful for real-time updates
Add below to VS Code settings.json for sse or http:
"mcpServers": { "mcp_a2a_gateway": { "url": "http://0.0.0.0:10000/mcp" } }
"mcpServers": { "mcp_a2a_gateway": { "type": "stdio", "command": "uvx", "args": ["mcp-a2a-gateway"], "env": { "MCP_TRANSPORT": "stdio", "MCP_DATA_DIR": "/Users/your-username/Desktop/data/Copilot/a2a_gateway/" } } }
"mcpServers": { "mcp_a2a_gateway": { "type": "stdio", "command": "uvx", "args": ["--from", "/path/to/MCP-A2A-Gateway", "mcp-a2a-gateway"], "env": { "MCP_TRANSPORT": "stdio", "MCP_DATA_DIR": "/Users/your-username/Desktop/data/Copilot/a2a_gateway/" } } }
"mcpServers": { "mcp_a2a_gateway": { "type": "stdio", "command": "uv", "args": [ "--directory", "/path/to/MCP-A2A-Gateway", "run", "mcp-a2a-gateway" ], "env": { "MCP_TRANSPORT": "stdio", "MCP_DATA_DIR": "/Users/your-username/Desktop/data/Copilot/a2a_gateway/" } } }
"mcpServers": { "mcp_a2a_gateway": { "command": "uvx", "args": ["mcp-a2a-gateway"], "env": { "MCP_TRANSPORT": "stdio", "MCP_DATA_DIR": "/Users/your-username/Desktop/data/Claude/a2a_gateway/" } } }
"mcpServers": { "mcp_a2a_gateway": { "command": "uvx", "args": ["--from", "/path/to/MCP-A2A-Gateway", "mcp-a2a-gateway"], "env": { "MCP_TRANSPORT": "stdio", "MCP_DATA_DIR": "/Users/your-username/Desktop/data/Claude/a2a_gateway/" } } }
"mcpServers": { "mcp_a2a_gateway": { "command": "uv", "args": ["--directory", "/path/to/MCP-A2A-Gateway", "run", "mcp-a2a-gateway"], "env": { "MCP_TRANSPORT": "stdio", "MCP_DATA_DIR": "/Users/your-username/Desktop/data/Claude/a2a_gateway/" } } }
The server exposes the following MCP tools for integration with LLMs like Claude:
-
register_agent: Register an A2A agent with the bridge server
{ "name": "register_agent", "arguments": { "url": "http://localhost:41242" } }
list_agents: Get a list of all registered agents
{ "name": "list_agents", "arguments": {"dummy": "" } }
unregister_agent: Remove an A2A agent from the bridge server
{ "name": "unregister_agent", "arguments": { "url": "http://localhost:41242" } }
-
send_message: Send a message to an agent and get a task_id for the response
{ "name": "send_message", "arguments": { "agent_url": "http://localhost:41242", "message": "What's the exchange rate from USD to EUR?", "session_id": "optional-session-id" } }
-
get_task_result: Retrieve a task's result using its ID
{ "name": "get_task_result", "arguments": { "task_id": "b30f3297-e7ab-4dd9-8ff1-877bd7cfb6b1", } }
get_task_list: Get a list of all tasks and their statuses.
{ "name": "get_task_list", "arguments": {} }
We are actively developing and improving the gateway! We welcome contributions of all kinds. Here is our current development roadmap, focusing on creating a rock-solid foundation first.
Core Stability & Developer Experience (Help Wanted! π)
This is our current focus. Our goal is to make the gateway as stable and easy to use as possible.
- Implement Streaming Responses: Full support for streaming responses from A2A agents.
- Enhance Error Handling: Provide clearer error messages and proper HTTP status codes for all scenarios.
- Input Validation: Sanitize and validate agent URLs during registration for better security.
- Add Health Check Endpoint: A simple/healthendpoint to monitor the server's status.
- Configuration Validation: Check for necessary environment variables at startup.
- Comprehensive Integration Tests: Increase test coverage to ensure reliability.
- Cancel Task: Implement task cancellation
- Implement Streaming Update: Implement streaming task update. So that user check the progress.
- Easy Installation: Add support foruvx
- Docker Support: Provide a Docker Compose setup for easy deployment.
- Better Documentation: Create a dedicated documentation site or expand the Wiki.
Want to contribute?Check out the issues tab or feel free to open a new one to discuss your ideas!
This project is licensed under the Apache License, Version 2.0 - see theLICENSEfile for details.
- Anthropic for theModel Context Protocol
- Google for theAgent-to-Agent Protocol
- Contributors to the FastMCP library
- Contributors ofA2A-MCP-Server(This project highly inspired from this repo.)
This project uses automated publishing through GitHub Actions for seamless releases.
Option 1: Using the Release Script (Recommended)
# Patch release (0.1.6 β 0.1.7) ./release.sh patch # Minor release (0.1.6 β 0.2.0) ./release.sh minor # Major release (0.1.6 β 1.0.0) ./release.sh major
- β
Check you're on the main branch with clean working directory
- π Automatically bump the version inpyproject.toml
- π¨ Build and test the package locally
- π€ Commit the version change and create a git tag
- π Push to GitHub, triggering automated PyPI publishing
# Update version in pyproject.toml manually # Then create and push a tag git add pyproject.toml git commit -m "chore: bump version to 0.1.7" git tag v0.1.7 git push origin main git push origin v0.1.7
To enable automated publishing, add your PyPI API token to GitHub Secrets:
- Go to your repository β Settings β Secrets and variables β Actions
- Add a new repository secret:
- Name:PYPI_API_TOKEN
- Value: Your PyPI token
- Push a tag or create a release
- Check the Actions tab for publishing status
For emergency releases or local testing:
# Build and get manual publish instructions ./publish.sh # Or publish directly (with credentials configured) uv build uv publish
An MCP server client for the Agent-to-Agent (A2A) protocol, enabling LLMs to interact with A2A agents.
A bridge server connecting Model Context Protocol (MCP) with Agent-to-Agent (A2A) protocol.
Production-grade multi-agent communication MCP server with 58 tools over MCP+SSE β real-time messaging, task scheduling, shared memory, and a trust-based evolution engine. SQLite WAL persistence, 4-level RBAC, zero-dependency Python/TypeScript SDKs.
Enables room-based messaging between multiple agents.
Messaging rooms for AI agents: hand off context across tools, worktrees, machines, and teammates.
Agent-to-agent messaging platform. any MCP-compatible agent sends and receives direct messages
Agent-to-agent messaging, trust attestation, and collaboration infrastructure β 20 tools + 8 resources for DMs, trust profiles, obligations, and agent discovery via Streamable HTTP.
Enables AI assistants to request human input through a web interface, facilitating human-in-the-loop interactions.
Agent-native collaboration network: orchestrate a team of long-running agents from any MCP client, with persistent identity, real-time messaging with @mentions and threads, task handoffs, shared workspace context, semantic search, and replayable MCP App widgets.
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.


