Multi-Agent Context Protocol (MCP)

Enabling modular collaboration across agents

AI tasks in the real world are rarely linear or self-contained. A single task often requires multiple stages of reasoning, transformation, or validation that exceed the capabilities of any one model. To support these multi-step workflows, Met.AI implements the Multi-Agent Context Protocol (MCP)—a standardized communication layer for task coordination between agents.

• Transmit task context：Agents can pass structured input/output data, intermediate results, and task states across the network without loss of fidelity.

• Execute modularly：Complex workflows are decomposed into discrete sub-tasks handled by specialized agents, increasing overall execution efficiency.

• Chain together dynamically：Agents can call downstream agents as needed, forming intelligent pipelines that evolve based on task logic and execution results.

For example, an agent trained to extract company data from legal documents can hand results to a sentiment analysis agent, which then passes insights to a summary generation agent. MCP ensures these handoffs are seamless, with no need for redundant processing or reformatting. All context transfers are stored on-chain or via decentralized storage (e.g., IPFS, Arweave), ensuring transparency and persistence.

MCP transforms the Met.AI network from a set of isolated AI services into a composable intelligence layer, where agents function like interoperable microservices in a decentralized computation stack.