Provides an MCP server that enables connecting to A2A Agents, managing conversations, and viewing agent cards and artifacts.
Configuration
View docs{
"mcpServers": {
"a2anet-a2a-mcp": {
"command": "uvx",
"args": [
"a2anet-mcp"
],
"env": {
"ANOTHER_VAR": "PLACEHOLDER",
"A2A_AGENT_CARDS": "[{\"url\": \"https://example.com/.well-known/agent-card.json\"}]"
}
}
}
}You run an MCP server that acts as an A2A Client for the A2A Protocol, enabling you to connect to remote A2A Agents, send messages, manage conversations, and view artifacts that would otherwise overload context. This guide shows practical steps to use, install, and configure the server so you can start integrating agents into your workflow.
You interact with the MCP server by connecting through an MCP client. Start the server instance using the provided runtime command, then configure your client to point at the server. Once connected, you can discover available agents, send messages to them, continue conversations, and inspect artifacts that help you reason about long or complex results.
Prerequisites: you need a compatible runtime (see the runtime setup below). Install the runtime if you haven’t already, then proceed to install and run the MCP server.
# Prerequisite: install the runtime (example shown for uvx-based runtime)
# Follow the runtime’s official installation steps if you don’t have it yet
# Install and run the MCP server using the provided package
# The command shown below is the runtime command and the MCP package name
uvxConfigure your client to load the MCP server by pointing to the Agent Card URL(s) that define the agents you want to connect with. You can specify one or more Agent Cards, each potentially including custom HTTP headers for authentication or other configuration.
{
"mcpServers": {
"a2a": {
"command": "uvx",
"args": ["a2anet-mcp"],
"env": {
"A2A_AGENT_CARDS": "[{\"url\": \"https://example.com/.well-known/agent-card.json\"}]"
}
}
}
}Use the available tools to discover agents, send messages, and view artifacts. This server stores conversations in JSON format for easy inspection and future reference.
Agent conversations are stored in JSON format at standard locations depending on your OS. This makes it easy to back up or inspect past interactions.
For local development, you can run the MCP server in a Claude Desktop workflow. Use the provided runtime command and environment variable to load your Agent Card(s) during development.
Discover available agents and their capabilities.
Send a message to a selected agent to continue a conversation.
View text content from an artifact with optional line range.
View structured data from an artifact with optional filtering.