How do I start automating AI ML API tasks with ai-ml-api-automation?

Add the MCP at https://rube.app/mcp, verify RUBE_SEARCH_TOOLS works, connect to the ai_ml_api toolkit with RUBE_MANAGE_CONNECTIONS, then run tools using RUBE_MULTI_EXECUTE_TOOL after confirming ACTIVE.

What should I do to keep tool schemas current?

Always call RUBE_SEARCH_TOOLS to fetch up-to-date tool slugs and input schemas; never hardcode slugs or arguments.

What is memory and why is it required?

Memory is included in RUBE_MULTI_EXECUTE_TOOL calls to maintain state within a session. Provide a memory object (even if empty) for each execution.

ai-ml-api-automation

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AI ML API Automation via Rube MCP

Automate AI ML API operations through Composio's AI ML API toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/ai_ml_api

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
Active AI ML API connection via RUBE_MANAGE_CONNECTIONS with toolkit ai_ml_api
Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works.

Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
Call RUBE_MANAGE_CONNECTIONS with toolkit ai_ml_api
If connection is not ACTIVE, follow the returned auth link to complete setup
Confirm connection status shows ACTIVE before running any workflows

Tool Discovery

Always discover available tools before executing workflows:

RUBE_SEARCH_TOOLS
queries: [{use_case: "AI ML API 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 AI ML API task"}]
session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["ai_ml_api"]
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

Operation	Approach
Find tools	`RUBE_SEARCH_TOOLS` with AI ML API-specific use case
Connect	`RUBE_MANAGE_CONNECTIONS` with toolkit `ai_ml_api`
Execute	`RUBE_MULTI_EXECUTE_TOOL` with discovered tool slugs
Bulk ops	`RUBE_REMOTE_WORKBENCH` with `run_composio_tool()`
Full schema	`RUBE_GET_TOOL_SCHEMAS` for tools with `schemaRef`

Powered by Composio

Source

git clone https://github.com/ComposioHQ/awesome-claude-skills/blob/master/composio-skills/ai-ml-api-automation/SKILL.mdView on GitHub

Overview

Automate AI/ML API operations using Composio's AI ML API toolkit through Rube MCP. The process emphasizes discovering tool schemas first with RUBE_SEARCH_TOOLS and ensuring an ACTIVE connection via RUBE_MANAGE_CONNECTIONS before executing workflows.

How This Skill Works

The workflow starts by discovering available AI ML API tools with RUBE_SEARCH_TOOLS to obtain current slugs and input schemas. It then verifies the toolkit connection with RUBE_MANAGE_CONNECTIONS and, once ACTIVE, executes tools via RUBE_MULTI_EXECUTE_TOOL using the discovered slug and schema-compliant arguments, while maintaining a session and including a memory object.

When to Use It

Automating AI/ML API operations end-to-end using the ai_ml_api toolkit via Rube MCP.
When tool schemas may change and you must fetch them with RUBE_SEARCH_TOOLS before each run.
When you need to ensure the ai_ml_api connection is ACTIVE via RUBE_MANAGE_CONNECTIONS.
When orchestrating a multi-step workflow that discovers, verifies, and executes one or more tools in a session.
When handling large results by paginating tool discovery responses and continuing until complete.

Quick Start

Step 1: Ensure RUBE_SEARCH_TOOLS is available to fetch current AI ML API tool schemas.
Step 2: Connect to the toolkit with RUBE_MANAGE_CONNECTIONS and follow any required auth if prompted.
Step 3: Confirm the connection is ACTIVE before running any workflows.

Best Practices

Always search first to get current schemas before running any workflow.
Verify the connection status is ACTIVE with RUBE_MANAGE_CONNECTIONS before executing tools.
Use exact field names and types from the search results; do not hardcode slugs or args.
Include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty.
Reuse session IDs within a workflow and handle pagination to fetch complete results.

Example Use Cases

Discover AI ML API tools with RUBE_SEARCH_TOOLS, connect to the ai_ml_api toolkit, and execute a chosen tool with the required arguments in a single session.
Batch multiple AI ML API tasks by iterating RUBE_MULTI_EXECUTE_TOOL calls within one session, passing appropriate memory between steps.
Reuse an existing session across several tool executions to compose a multi-step AI ML API workflow.
Perform bulk operations using RUBE_REMOTE_WORKBENCH and run_composio_tool() to execute multiple discovered tools efficiently.
Fetch full tool schemas using RUBE_GET_TOOL_SCHEMAS to validate inputs and ensure schema compliance before execution.

Frequently Asked Questions

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