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bigml-automation

Automate Bigml tasks via Rube MCP (Composio). Always search tools first for current schemas.

Author

@ComposioHQ

Category

Development Tools

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BigML Automation - Automating BigML via Rube MCP

Skill Overview

BigML Automation is a Claude skill that automates the BigML machine learning platform through Composio’s Rube MCP service. It supports dynamic tool discovery, connection management, and batch execution of operations.

Use Cases

  • Machine learning workflow automation: Automatically run tasks such as model training, evaluation, and deployment on the BigML platform, without manual intervention.

  • Dynamic tool integration: Use RUBE_SEARCH_TOOLS to discover available BigML tools dynamically, adapting to API schema updates.

  • No-key integration development: Use the Rube MCP endpoint to enable zero-configuration BigML connections, without managing an API Key.

    • Core Features

  • Dynamic tool discovery: Use RUBE_SEARCH_TOOLS to fetch BigML tool slugs, input schemas, and execution plans in real time, ensuring the latest tool definitions are always used.

  • Connection state management: Use RUBE_MANAGE_CONNECTIONS to check and manage BigML connection status, supporting quick authorization via the provided link.

  • Batch tool execution: Use RUBE_MULTI_EXECUTE_TOOL to perform batch execution of multiple BigML operations, supporting session reuse and memory management.

    • Common Questions

    How do I connect to BigML using Rube MCP?

    First, add https://rube.app/mcp as an MCP server, then call RUBE_SEARCH_TOOLS to verify the connection. Next, use RUBE_MANAGE_CONNECTIONS to configure the BigML tool set. If the connection is not activated, complete authorization using the link returned by the response.

    How can I get the BigML tool schema?

    Before each workflow execution, you must call RUBE_SEARCH_TOOLS to retrieve the latest tool schema. This is because BigML tool definitions may be updated at any time; hard-coding can cause calls to fail. The search results will return the tool’s input fields, types, and recommended execution plan.

    When should the session ID be reused?

    Within the same workflow, you should reuse the session ID to maintain context and connection state. When starting a new independent workflow, generate a new session ID. Also note that in RUBE_MULTI_EXECUTE_TOOL calls, you should always include the memory parameter—even if it is an empty object {}.