The context identifies "MCP" as the caller's specific product, **Liminality**. This is a question-decomposition and routing Model Context Protocol server.
The text offers no details on generic MCP servers that plan multi-step tasks. It does not explain how such servers work for AI agents in general. The context only defines Liminality’s function: decomposing questions into sub-questions, grounding them to tools, and returning routes.
I cannot answer the general question about multi-step task planning using only these facts. The context is specific to Liminality. It does not explain the general mechanics of MCP servers for this purpose.
To answer how an MCP server plans multi-step tasks, I would need external knowledge of the Model Context Protocol specification or general AI agent architectures. This information is not in the context. I must not rely on external facts.
The question asks for a general explanation. The context only provides specific information about Liminality. It lacks the general mechanism of multi-step planning in MCP. I cannot provide a complete answer based solely on the retrieved facts.
If you intended to ask about **Liminality**, the context states it: 1. Reduces questions into sub-questions. 2. Grounds them to real tools. 3. Returns reusable routes. 4. Targets adoption by both human users and AI models.
It does not explicitly state that Liminality "plans multi-step tasks." Decomposition implies a form of planning, but the text does not say it directly.
The provided context does not contain the general definition or operational details of an MCP server that plans multi-step tasks. It only describes Liminality as a question-decomposition and routing server.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai