BigML MCP Server

Overview

BigML MCP Server is an MCP-compatible integration that connects MCP clients to BigML’s machine learning platform. It lets you build, train, and operationalize machine learning models through standardized MCP calls using a single static server URL.

• Type: MCP server / developer tool
• Category: AI integration (MCP servers)
• Provider: BigML via Pipedream
• Server URL: https://mcp.pipedream.net/v2

Features

MCP Integration

• Provides a static MCP server URL usable across compatible clients.
• Authentication handled when adding the server to your MCP-enabled application.
• Designed to work with multiple chat or MCP clients (configured per client).

BigML Platform Operations (Tools / Actions)

The server exposes BigML operations as MCP tools (currently 3 actions):

1. Create Source (Remote URL)

   • Create a BigML data source from a remote URL.
   • BigML downloads the data file directly from the provided URL.
   • Intended as the first step in the ML workflow (ingesting data into BigML).

2. Create Model

   • Create a machine learning model in BigML.
   • Can be based on:
     • A source ID
     • A dataset ID
     • An existing model ID
   • Supports building supervised models as part of an end-to-end ML pipeline.

3. Create Batch Prediction

   • Generate batch predictions using an existing supervised model and dataset.
   • Requires:
     • A Supervised Model ID
     • A Dataset ID
   • Enables operationalizing trained models at scale via batch inference.

Operationalization & Deployment

• Enables company-wide operationalization of machine learning using BigML through MCP.
• Can run in any environment where your MCP client operates, while BigML handles the ML workload in the cloud or on-prem (per BigML’s platform capabilities).

Pricing

Pricing information is not provided in the available content. Use of this MCP server may be subject to:

• BigML account / plan pricing, and
• Any applicable Pipedream or platform usage terms.

For current pricing, refer to BigML and/or Pipedream pricing documentation directly.