A2A MCP Bridge Server

Overview

The A2A MCP Server solves the friction that arises when an AI assistant built on Anthropic’s Model Context Protocol (MCP) needs to orchestrate tasks across a fleet of autonomous agents that speak the Google‑developed Agent‑to‑Agent (A2A) protocol. By acting as a bidirectional translator, the server allows MCP clients—such as Claude Desktop—to discover, register, and command A2A agents through a single, consistent interface. This eliminates the need for custom adapters or manual JSON‑RPC plumbing and ensures that developers can treat a heterogeneous set of agents as a unified resource pool.

At its core, the server exposes three high‑level capabilities. First, Agent Management lets users register new A2A agents, enumerate the ones currently available, and cleanly unregister them when they’re no longer needed. Second, Communication provides a straightforward send‑and‑receive API that forwards messages to the target agent and streams back responses in real time, preserving the low‑latency feel that MCP clients expect. Third, Task Management tracks long‑running operations by task ID, allowing callers to query status, retrieve results, or cancel executions midstream. Together, these functions give developers a powerful toolbox for choreographing complex workflows that span multiple specialized agents.

The server’s transport flexibility is a key advantage. It supports three modes—stdio for local command‑line use, streamable‑HTTP for web or cloud deployments, and SSE for lightweight event streams. This design means a single binary can serve both local prototyping environments and production APIs without code changes, simply by toggling an environment variable. The ability to stream responses also aligns with MCP’s emphasis on composable, real‑time interactions, enabling assistants to present partial results as they arrive.

Real‑world scenarios that benefit from this bridge include financial data aggregation, where an assistant pulls currency conversion rates from a dedicated “Currency Agent,” or multi‑step scientific pipelines, where one agent retrieves experimental data while another processes it. In each case, the MCP client can issue a single high‑level request and let the A2A agents handle the specialized logic, all while maintaining a clean separation of concerns. The server’s unified API also simplifies monitoring and logging, as all agent interactions funnel through a single MCP endpoint.

In summary, the A2A MCP Server removes protocol barriers between Anthropic’s and Google’s agent ecosystems. It gives developers a single, well‑defined entry point to register agents, dispatch tasks, and stream results—all while supporting multiple transport layers for local testing or scalable cloud deployments. This integration unlocks richer, more modular AI applications that can leverage the best capabilities of both MCP and A2A.