Always discover available tools before executing workflows:

RUBE_SEARCH_TOOLS
queries: [{use_case: "Kaggle operations", known_fields: ""}]
session: {generate_id: true}

This returns available tool slugs, input schemas, recommended execution plans, and known pitfalls.

Core Workflow Pattern

Step 1: Discover Available Tools

RUBE_SEARCH_TOOLS
queries: [{use_case: "your specific Kaggle task"}]
session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["kaggle"]
session_id: "your_session_id"

Step 3: Execute Tools

RUBE_MULTI_EXECUTE_TOOL
tools: [{
  tool_slug: "TOOL_SLUG_FROM_SEARCH",
  arguments: {/* schema-compliant args from search results */}
}]
memory: {}
session_id: "your_session_id"

Known Pitfalls

• Always search first: Tool schemas change. Never hardcode tool slugs or arguments without calling RUBE_SEARCH_TOOLS
• Check connection: Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before executing tools
• Schema compliance: Use exact field names and types from the search results
• Memory parameter: Always include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty ({})
• Session reuse: Reuse session IDs within a workflow. Generate new ones for new workflows
• Pagination: Check responses for pagination tokens and continue fetching until complete

Quick Reference

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ai-ml-api-automation

通过 Rube MCP 自动化执行 AI/ML API 任务，并先检索最新工具 schema。