What is Rube MCP and how does it enable Kontent AI automation?

Rube MCP is the manager that discovers, connects, and executes Kontent AI tools. You must connect to Kontent AI via RUBE_MANAGE_CONNECTIONS and rely on RUBE_SEARCH_TOOLS to fetch up-to-date tool schemas before execution.

Why must I call RUBE_SEARCH_TOOLS before executing any tool?

Tool schemas change over time. Fetching current schemas ensures you use valid slugs and correctly typed input fields, preventing run-time errors.

What should I include in the memory parameter during execution?

Always include a memory object in RUBE_MULTI_EXECUTE_TOOL calls, even if empty, to preserve session state and support complex workflows.

kontent-ai-automation

Scanned

npx machina-cli add skill ComposioHQ/awesome-claude-skills/kontent-ai-automation --openclaw

Files (1)

SKILL.md

2.9 KB

Kontent AI Automation via Rube MCP

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

Toolkit docs: composio.dev/toolkits/kontent_ai

Prerequisites

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

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["kontent_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

Operation	Approach
Find tools	`RUBE_SEARCH_TOOLS` with Kontent AI-specific use case
Connect	`RUBE_MANAGE_CONNECTIONS` with toolkit `kontent_ai`
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/kontent-ai-automation/SKILL.mdView on GitHub

Overview

Automates Kontent AI operations through Composio's Kontent AI toolkit via Rube MCP. It requires an active Kontent AI connection and mandates calling RUBE_SEARCH_TOOLS first to fetch current tool schemas. This keeps tool slugs and input schemas up to date and prevents hardcoding.

How This Skill Works

You first discover available Kontent AI tools with RUBE_SEARCH_TOOLS to obtain current input schemas. Next, verify or establish the Kontent AI connection with RUBE_MANAGE_CONNECTIONS. Finally, execute the selected tool using RUBE_MULTI_EXECUTE_TOOL, including a memory payload and a valid session_id.

When to Use It

Starting a new Kontent AI automation workflow from scratch and needing up-to-date tool schemas
Automating a specific Kontent AI task after discovering the appropriate tool and its required inputs
Verifying an active Kontent AI connection before running any Kontent AI workflows
Handling tool schema changes by re-fetching current tools before execution
Running multiple Kontent AI tasks in a single session or performing bulk operations

Quick Start

Step 1: Add https://rube.app/mcp as an MCP server in your client config
Step 2: Run RUBE_SEARCH_TOOLS to confirm availability and fetch current Kontent AI tool schemas
Step 3: Run RUBE_MANAGE_CONNECTIONS for the kontent_ai toolkit; if not ACTIVE, complete authentication and then use RUBE_MULTI_EXECUTE_TOOL with a discovered tool slug and memory

Best Practices

Always call RUBE_SEARCH_TOOLS first to fetch current Kontent AI tool schemas
Check that RUBE_MANAGE_CONNECTIONS shows ACTIVE before executing tools
Use exact field names and types from the tool schemas; do not hardcode slugs or args
Include a memory object in every RUBE_MULTI_EXECUTE_TOOL call, even if empty
Reuse session IDs within a workflow and fetch additional pages if results are paginated

Example Use Cases

End-to-end Kontent AI workflow: discover tools, establish ACTIVE connection, then execute a publishing tool in one session
Bulk Kontent AI operations using RUBE_REMOTE_WORKBENCH and run_composio_tool for multiple tasks
Schema-aware automation: always fetch tool schemas with RUBE_SEARCH_TOOLS before selecting a tool slug
Pagination handling: iterate through paginated tool lists to ensure complete coverage
Validation-first workflow: confirm ACTIVE connection via RUBE_MANAGE_CONNECTIONS before any multi-tool execution

Frequently Asked Questions

Add this skill to your agents