A2AMCP Server

A2AMCP – Agent‑to‑Agent Model Context Protocol
A2AMCP extends the Model Context Protocol (MCP) by embedding Google’s Agent‑to‑Agent communication patterns directly into a lightweight, Redis‑backed server. The core problem it addresses is the isolation of AI assistants that simultaneously edit or reason about a shared codebase. When agents operate independently, they frequently overwrite each other’s work, duplicate effort, and generate merge conflicts that would otherwise be avoided through human collaboration. A2AMCP solves this by providing a real‑time coordination layer that lets agents exchange intents, lock files, and share contract definitions before any changes are committed.

At its heart, the server exposes a rich set of MCP tools that enable direct inter‑agent messaging, broadcast notifications, and asynchronous task queues. Agents can query each other for the latest interface definitions, negotiate file ownership through automatic locking mechanisms, and signal task completion to a shared todo list. These capabilities make the server invaluable for developers who want their AI assistants to act as a cohesive team rather than isolated workers. By centralizing context management, A2AMCP ensures that every agent has a consistent view of the project’s API contracts and dependency graph, reducing friction in multi‑service or full‑stack development scenarios.

Key features include:

• Real‑time communication: Agents can send messages or broadcast status updates without latency, enabling instant conflict resolution.
• File conflict prevention: Automatic locking and negotiation prevent simultaneous edits to the same file, while a conflict detection engine alerts agents before merge issues arise.
• Shared context registry: A global interface/type store allows one agent to publish an API contract that others can consume, fostering a plug‑and‑play development workflow.
• Task transparency: A built‑in todo list tracks progress, visibility, and completion signals across agents, giving developers a clear picture of ongoing work.
• Multi‑project isolation: Each project is sandboxed in its own namespace, with Redis persistence and automatic cleanup to keep the environment tidy.

Typical use cases span from microservices orchestration, where distinct agents build separate services and need to agree on contract boundaries, to documentation generation pipelines that require coordinated updates across interdependent modules. Testing teams can also leverage A2AMCP to sync test writers with feature developers, ensuring that new tests align with the latest API changes. Because the server follows MCP SDK 1.9.3 conventions and implements all 17 required tools, it integrates seamlessly into existing AI workflows—whether agents are invoked via Claude Code CLI, the desktop client, or custom scripts.

In summary, A2AMCP transforms a collection of autonomous AI assistants into a synchronized development squad. By providing real‑time coordination, conflict avoidance, and shared context management, it eliminates the headaches of parallel editing and unlocks new productivity gains for teams that rely on AI‑powered code generation, testing, or documentation.