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Rube MCP AI ML API Automation
The prompt instructs on automating AI ML API operations through Composio's AI ML API toolkit via Rube MCP, covering prerequisites, setup steps, tool discovery with RUBE_SEARCH_TOOL…
- External action: high
SKILL 1 file
SKILL.md
---
name: ai-ml-api-automation
description: "Automate AI ML API tasks via Rube MCP (Composio). Always search tools first for current schemas."
---
# 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](https://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.
1. Verify Rube MCP is available by confirming `RUBE_SEARCH_TOOLS` responds
2. Call `RUBE_MANAGE_CONNECTIONS` with toolkit `ai_ml_api`
3. If connection is not ACTIVE, follow the returned auth link to complete setup
4. 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](https://composio.dev)*
REQUIRED CONTEXT
- Rube MCP connection status
- active ai_ml_api connection
TOOLS REQUIRED
- RUBE_SEARCH_TOOLS
- RUBE_MANAGE_CONNECTIONS
- RUBE_MULTI_EXECUTE_TOOL
- RUBE_GET_TOOL_SCHEMAS
- RUBE_REMOTE_WORKBENCH
ROLES & RULES
- Always search tools first for current schemas
- Always call RUBE_SEARCH_TOOLS first to get current tool schemas
- Always discover available tools before executing workflows
- Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
- Confirm connection status shows ACTIVE before running any workflows
- Never hardcode tool slugs or arguments without calling RUBE_SEARCH_TOOLS
- Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before executing tools
- Use exact field names and types from the search results
- Always include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty
- Reuse session IDs within a workflow
- Generate new session IDs for new workflows
- Check responses for pagination tokens and continue fetching until complete
EXPECTED OUTPUT
- Format
- markdown
- Constraints
- always search tools first
- include exact function call examples
- list known pitfalls
SUCCESS CRITERIA
- Search tools first using RUBE_SEARCH_TOOLS
- Verify ACTIVE connection status via RUBE_MANAGE_CONNECTIONS
- Execute only schema-compliant tool calls via RUBE_MULTI_EXECUTE_TOOL
- Reuse session IDs within workflows
FAILURE MODES
- Hardcoding tool slugs without prior RUBE_SEARCH_TOOLS call
- Executing tools before confirming ACTIVE connection
- Omitting the memory parameter in RUBE_MULTI_EXECUTE_TOOL
- Failing to handle pagination tokens in responses
EXAMPLES
Includes multiple example RUBE_* tool calls with queries, arguments, and session handling for search, connection, and execution steps.
CAVEATS
- Dependencies
- Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
- Active AI ML API connection via RUBE_MANAGE_CONNECTIONS with toolkit ai_ml_api
- Missing context
- Target model or agent that will consume the prompt
- Example concrete use-case values for the queries
- Ambiguities
- Placeholder text "your specific AI ML API task" is not marked as replaceable
- Does not specify desired output length or format for workflow results
QUALITY
- OVERALL
- 0.79
- CLARITY
- 0.85
- SPECIFICITY
- 0.78
- REUSABILITY
- 0.72
- COMPLETENESS
- 0.80
IMPROVEMENT SUGGESTIONS
- Wrap replaceable strings in {{double_braces}} and list them under a Variables section
- Add one fully-worked example call for a common operation such as model inference
USAGE
Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.
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