todo-ai-mcp-agent

Natural language todo management uses MCP tools to convert user requests into structured actions. This Skill enables intent parsing and safe tool invocation—mapping phrases like 'create a task' to MCP todo operations, then grounding results in actual data returned from the tools.

Stateless design maintains session context without persistent memory between turns. Each conversation includes explicit tool schemas and safety patterns that ensure the agent validates requests and grounds actions in tool results, preventing hallucination or unsafe invocations across multiple steps.

Safe mapping requires explicit tool schemas, validation logic, and grounding to actual tool results. This Skill covers intent interpretation patterns, tool-result verification, and safety constraints that ensure each natural language request translates to a valid, auditable MCP operation before execution.

Yes. Multi-step workflows use explicit tool mappings and session-context handling to disambiguate requests across turns. The Skill's safety-first design validates each step, grounds outcomes in tool results, and surfaces ambiguity so the agent can clarify or refine the user's intent.

You need MCP tool schemas for todo operations (list, create, update, delete), explicit tool-to-intent mappings, and a safety validation layer. The Skill requires stateless session-context handling and grounding patterns to ensure conversational accuracy and auditability.