Google Cloud Vision Automation

What This Skill Does

• This skill automates Google Cloud Vision tasks using the Composio Google Cloud Vision toolkit via Rube MCP.
• It's designed for developers and data scientists who need to programmatically analyze images using Google's Cloud Vision API.

When to Use

• Detect objects in images.
• Extract text from images (OCR).
• Identify faces and landmarks.
• Analyze image sentiment.
• Moderate inappropriate content.
• Automate image tagging.

Key Features

• Automates Google Cloud Vision operations.
• Uses Rube MCP and Composio toolkit.
• Requires an active Google Cloud Vision connection.
• Uses RUBE_SEARCH_TOOLS for tool discovery.
• Employs RUBE_MULTI_EXECUTE_TOOL for execution.
• Uses composio.

Manual Installation

Manual installationView Full Skill ContentThe complete markdown content that gets installedGoogle Cloud Vision Automation via Rube MCP

Automate Google Cloud Vision operations through Composio's Google Cloud Vision toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/google_cloud_vision

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available) Active Google Cloud Vision connection via RUBE_MANAGE_CONNECTIONS with toolkit google_cloud_vision Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

• Get Rube MCP: Add https://rube.
• 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 google_cloud_vision 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: "Google Cloud Vision 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 Google Cloud Vision task"}] session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS toolkits: ["google_cloud_vision"] 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