mistral_ai-automation
Scannednpx machina-cli add skill ComposioHQ/awesome-claude-skills/mistral_ai-automation --openclawMistral AI Automation via Rube MCP
Automate Mistral AI operations through Composio's Mistral AI toolkit via Rube MCP.
Toolkit docs: composio.dev/toolkits/mistral_ai
Prerequisites
- Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
- Active Mistral AI connection via
RUBE_MANAGE_CONNECTIONSwith toolkitmistral_ai - Always call
RUBE_SEARCH_TOOLSfirst 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_TOOLSresponds - Call
RUBE_MANAGE_CONNECTIONSwith toolkitmistral_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": "completions, embeddings, fine-tuning, and model management", "known_fields": ""}]
This returns:
- Available tool slugs for Mistral AI
- Recommended execution plan steps
- Known pitfalls and edge cases
- Input schemas for each tool
Core Workflows
1. Discover Available Mistral AI Tools
RUBE_SEARCH_TOOLS:
queries:
- use_case: "list all available Mistral AI tools and capabilities"
Review the returned tools, their descriptions, and input schemas before proceeding.
2. Execute Mistral AI Operations
After discovering tools, execute them via:
RUBE_MULTI_EXECUTE_TOOL:
tools:
- tool_slug: "<discovered_tool_slug>"
arguments: {<schema-compliant arguments>}
memory: {}
sync_response_to_workbench: false
3. Multi-Step Workflows
For complex workflows involving multiple Mistral AI operations:
- Search for all relevant tools:
RUBE_SEARCH_TOOLSwith specific use case - Execute prerequisite steps first (e.g., fetch before update)
- Pass data between steps using tool responses
- Use
RUBE_REMOTE_WORKBENCHfor bulk operations or data processing
Common Patterns
Search Before Action
Always search for existing resources before creating new ones to avoid duplicates.
Pagination
Many list operations support pagination. Check responses for next_cursor or page_token and continue fetching until exhausted.
Error Handling
- Check tool responses for errors before proceeding
- If a tool fails, verify the connection is still ACTIVE
- Re-authenticate via
RUBE_MANAGE_CONNECTIONSif connection expired
Batch Operations
For bulk operations, use RUBE_REMOTE_WORKBENCH with run_composio_tool() in a loop with ThreadPoolExecutor for parallel execution.
Known Pitfalls
- Always search tools first: Tool schemas and available operations may change. Never hardcode tool slugs without first discovering them via
RUBE_SEARCH_TOOLS. - Check connection status: Ensure the Mistral AI connection is ACTIVE before executing any tools. Expired OAuth tokens require re-authentication.
- Respect rate limits: If you receive rate limit errors, reduce request frequency and implement backoff.
- Validate schemas: Always pass strictly schema-compliant arguments. Use
RUBE_GET_TOOL_SCHEMASto load full input schemas whenschemaRefis returned instead ofinput_schema.
Quick Reference
| Operation | Approach |
|---|---|
| Find tools | RUBE_SEARCH_TOOLS with Mistral AI-specific use case |
| Connect | RUBE_MANAGE_CONNECTIONS with toolkit mistral_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 |
Toolkit docs: composio.dev/toolkits/mistral_ai
Source
git clone https://github.com/ComposioHQ/awesome-claude-skills/blob/master/composio-skills/mistral_ai-automation/SKILL.mdView on GitHub Overview
Automate Mistral AI operations using Composio's Mistral AI toolkit through Rube MCP. It covers completions, embeddings, fine-tuning, and model management, with a strong emphasis on discovering current tool schemas first to stay in sync.
How This Skill Works
Connect Rube MCP to the Mistral AI toolkit, then use RUBE_SEARCH_TOOLS to discover available tools and their input schemas. Execute the chosen tools with RUBE_MULTI_EXECUTE_TOOL, and for complex workflows, orchestrate steps with RUBE_REMOTE_WORKBENCH or pass data between tool responses. Ensure the Mistral AI connection remains ACTIVE via RUBE_MANAGE_CONNECTIONS before running workflows.
When to Use It
- You need to run large-scale completions or embeddings and want an automated, schema-aware workflow.
- You plan to fine-tune a Mistral AI model programmatically using a multi-step process.
- You need to manage Mistral AI models, versions, or configurations through the toolkit.
- You want to orchestrate multi-step workflows that span discovery, execution, and bulk operations.
- You must keep tool schemas current and validated by discovering them before actions.
Quick Start
- Step 1: Add the MCP server (https://rube.app/mcp) to your client configuration; no API keys needed.
- Step 2: Verify connectivity with RUBE_SEARCH_TOOLS and establish an ACTIVE Mistral AI connection via RUBE_MANAGE_CONNECTIONS with toolkit mistral_ai.
- Step 3: Discover tools using RUBE_SEARCH_TOOLS, then execute tools with RUBE_MULTI_EXECUTE_TOOL (or use RUBE_REMOTE_WORKBENCH for bulk workflows).
Best Practices
- Always call RUBE_SEARCH_TOOLS first to fetch current tool slugs and input schemas.
- Verify the Mistral AI connection is ACTIVE via RUBE_MANAGE_CONNECTIONS before workflows; re-authenticate if needed.
- Use RUBE_GET_TOOL_SCHEMAS when schemaRef is returned to load full input schemas and ensure schema-compliant arguments.
- Implement robust error handling and backoff for rate limits; check tool responses for errors before proceeding.
- For bulk or multi-step operations, use RUBE_REMOTE_WORKBENCH and pass data between steps via tool responses.
Example Use Cases
- Discover available Mistral AI tools and run a batch of completions for a list of prompts.
- Compute embeddings for a corpus and index results into a vector store in parallel.
- Orchestrate a fine-tuning workflow that prepares data, initiates training, and validates results.
- Update model metadata and configurations across versions using RUBE_MANAGE_CONNECTIONS and subsequent tool executions.
- Bulk process thousands of prompts with parallel tool execution using RUBE_REMOTE_WORKBENCH.