Convolo AI Automation

What This Skill Does

• This skill automates Convolo AI operations through the Composio Convolo AI toolkit via Rube MCP.
• It's designed for users looking to integrate and manage Convolo AI functionalities programmatically.
• The skill emphasizes proper setup, tool discovery, and execution while avoiding common mistakes.

When to Use

• Automate AI model training.
• Deploy AI models to production.
• Monitor AI model performance.
• Trigger alerts for AI model degradation.
• Integrate AI insights into workflows.

Key Features

• Uses Rube MCP for Convolo AI automation.
• Requires active Convolo AI connection.
• Employs RUBE_SEARCH_TOOLS for tool discovery.
• Provides a core workflow pattern for execution.
• Highlights common pitfalls and solutions.
• Uses Composio's Convolo AI toolkit.

Manual Installation

• Manual installation
• View Full Skill Content
• The complete markdown content that gets installed
• Convolo AI Automation via Rube MCP

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

Toolkit docs: composio.dev/toolkits/convolo_ai

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available) Active Convolo AI connection via RUBE_MANAGE_CONNECTIONS with toolkit convolo_ai 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 convolo_ai 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: "Convolo AI 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 Convolo AI task"}] session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS toolkits: ["convolo_ai"] 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

• Powered by Composio
• Convolo AI Automation via Rube MCP

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

Toolkit docs: composio.dev/toolkits/convolo_ai

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

• 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 convolo_ai If connection is not ACTIVE, follow the returned auth link to complete setup Confirm connection status shows ACTIVE before running any workflows

Always discover available tools before executing workflows:

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

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

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

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

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

• 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

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