ai-agent-mcp.json

{
  "name": "my_first_agent",
  "description": "AI agent that discovers MCP tools, plans, executes, and summarizes",
  "version": 1,
  "schemaVersion": 2,
  "inputParameters": ["task"],
  "tasks": [
    {
      "name": "discover_tools",
      "taskReferenceName": "discover",
      "type": "LIST_MCP_TOOLS",
      "inputParameters": {
        "mcpServer": "http://localhost:3001/mcp"
      }
    },
    {
      "name": "plan_action",
      "taskReferenceName": "plan",
      "type": "LLM_CHAT_COMPLETE",
      "inputParameters": {
        "llmProvider": "openai",
        "model": "gpt-4o-mini",
        "messages": [
          {
            "role": "system",
            "message": "You are an AI agent. Available tools: ${discover.output.tools}. Pick exactly one tool and respond as JSON with fields `method` and `arguments`."
          },
          {
            "role": "user",
            "message": "${workflow.input.task}"
          }
        ],
        "temperature": 0.1,
        "maxTokens": 500
      }
    },
    {
      "name": "execute_tool",
      "taskReferenceName": "execute",
      "type": "CALL_MCP_TOOL",
      "inputParameters": {
        "mcpServer": "http://localhost:3001/mcp",
        "method": "${plan.output.result.method}",
        "arguments": "${plan.output.result.arguments}"
      }
    },
    {
      "name": "summarize_result",
      "taskReferenceName": "summarize",
      "type": "LLM_CHAT_COMPLETE",
      "inputParameters": {
        "llmProvider": "openai",
        "model": "gpt-4o-mini",
        "messages": [
          {
            "role": "user",
            "message": "The user asked: \"${workflow.input.task}\". Tool returned: ${execute.output.content}. Reply in one short paragraph."
          }
        ],
        "maxTokens": 500
      }
    }
  ],
  "outputParameters": {
    "plan": "${plan.output.result}",
    "toolResult": "${execute.output.content}",
    "summary": "${summarize.output.result}"
  }
}