mcp-for-agents

What problem does it solve?

It helps developers design or repair MCP servers so AI agents can reliably choose the right tools, pass correct parameters, and get actionable results without wasting tokens or breaking orchestration.

Core Features & Use Cases

• Outcome-oriented tool design: collapse multi-step REST flows into single agent-call outcomes like track_latest_order(email) so agents do not chain tools unnecessarily.
• Agent-friendly parameter schemas: flatten parameters to top-level primitives, add enums/defaults, and ensure consistent naming to reduce hallucinated or missing fields.
• High-signal tool descriptions: write descriptions that clearly state when to use the tool, what it cannot do, key parameters, and example triggers so the agent selects correctly.
• Actionable error handling: return tool execution failures using isError: true with correction-oriented messages rather than protocol errors or raw backend details.
• Token-efficient and composable outputs: minimize context bloat via constrained tool surfaces, pagination defaults, and structured outputs the next tool can consume directly.
• Read/write separation and predictable naming: distinguish get/list/search from create/update/delete, and prefix names to avoid collisions across multiple servers.

Quick Start

Ask an AI assistant to review your MCP tool surface and rewrite your tool schemas, descriptions, and isError behaviors to make the tools outcome-oriented, flat-parameter, token-efficient, and agent-selectable.