How to integrate Bigml MCP with Mastra AI

This guide walks you through connecting Bigml to Mastra AI using the Composio tool router. By the end, you'll have a working Bigml agent that can create a new bigml project for customer data, list all correlations available in your account, get details for a specific bigml project through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a Bigml account through Composio's Bigml MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.

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Introduction

This guide walks you through connecting Bigml to Mastra AI using the Composio tool router. By the end, you'll have a working Bigml agent that can create a new bigml project for customer data, list all correlations available in your account, get details for a specific bigml project through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Bigml account through Composio's Bigml MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Bigml with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKLlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Bigml tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Bigml tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Bigml agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Bigml MCP server, and what's possible with it?

The Bigml MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bigml account. It provides structured and secure access to your machine learning environment, so your agent can perform actions like creating projects, managing data connectors, inspecting resources, and analyzing correlations on your behalf.

  • Project creation and organization: Easily direct your agent to create new projects to group related BigML resources for streamlined workflows.
  • External data connector management: Have your agent set up and retrieve external connectors to bring in data from external sources and databases.
  • Resource inspection and retrieval: Let your agent fetch detailed metadata about projects or connectors, helping you monitor and audit your ML assets.
  • Automated project cleanup: Instruct your agent to delete obsolete or unused projects, ensuring your workspace stays organized and efficient.
  • Correlation browsing and analysis: Ask your agent to list and paginate correlation resources, uncovering relationships among your datasets for deeper insights.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Bigml through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for Bigml

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["bigml"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Bigml MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "bigml" for Bigml access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Bigml toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "bigml-mastra-agent",
    instructions: "You are an AI agent with Bigml tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        bigml: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Bigml toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Bigml and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["bigml"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      bigml: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "bigml-mastra-agent",
    instructions: "You are an AI agent with Bigml tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { bigml: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Bigml through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows
TOOLS

Supported Tools

Every Bigml action and event your agent gets out of the box.

Create External Connector

Tool to create a new external connector for data sources.

Create Project

Tool to create a new project.

Delete Project

Tool to delete an existing project.

Get Configuration

Retrieves complete details of a BigML configuration by its ID to get stored parameters.

Get External Connector

Retrieves complete details of a BigML external connector by its ID.

Get Project

Tool to retrieve details of a project by ID.

Get Source

Retrieves complete details of a BigML source by its ID.

List Anomaly Detectors

Tool to list anomaly detector resources in your account.

List Anomaly Scores

Tool to list anomaly score resources.

List Associations

Tool to list association resources.

List Association Sets

Tool to list association set resources in your account.

List Batch Anomaly Scores

Tool to list batch anomaly score resources.

List Batch Centroids

Tool to list all batch centroid resources in your account with support for filtering, ordering, and pagination.

List Batch Predictions

Tool to list batch prediction resources.

List Batch Projections

Tool to list batch projection resources with support for filtering, ordering, and pagination.

List Batch Topic Distributions

Tool to list batch topic distribution resources.

List Centroids

Tool to list centroid resources.

List Clusters

Tool to list cluster resources with support for filtering, ordering, and pagination.

List Composites

Tool to list composite source resources.

List Configurations

Tool to list all configuration resources in your account.

List Correlations

Tool to list correlation resources.

List Datasets

Tool to list dataset resources.

List Deepnets

Tool to list deep neural network resources.

List Ensembles

Tool to list ensemble resources with filtering, ordering, and pagination support.

List Evaluations

Tool to list evaluation resources.

List Executions

Tool to list execution resources.

List Forecasts

Tool to list forecast resources.

List Fusions

Tool to list fusion resources.

List Libraries

Tool to list WhizzML library resources.

List Linear Regressions

Tool to list linear regression resources.

List Logistic Regressions

Tool to list logistic regression resources.

List Models

Tool to list model resources.

List OptiMLs

Tool to list OptiML resources in your account.

List PCAs

Tool to list PCA resources.

List Predictions

Tool to list prediction resources.

List Projections

Tool to list projection resources with support for filtering, ordering, and pagination.

List Projects

Tool to list all project resources in your account with support for filtering, ordering, and pagination.

List Samples

Tool to list sample resources.

List Scripts

Tool to list WhizzML script resources.

List Sources

Tool to list source resources in your account.

List Statistical Tests

Tool to list statistical test resources.

List Time Series

Tool to list time series resources.

List Topic Distributions

Tool to list topic distribution resources.

List Topic Models

Tool to list topic model resources.

Update Source

Tool to update a source's name, description, tags, or parsing configuration.

FRAMEWORKS

How to build Bigml MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Bigml MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Bigml MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Bigml MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Bigml MCP with Claude Cowork

Codex logo

Codex

Use Bigml MCP with Codex

OpenClaw logo

OpenClaw

Use Bigml MCP with OpenClaw

Hermes logo

Hermes

Use Bigml MCP with Hermes

CLI logo

CLI

Use Bigml MCP with CLI

Google ADK logo

Google ADK

Use Bigml MCP with Google ADK

LangChain logo

LangChain

Use Bigml MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Bigml MCP with Vercel AI SDK

LlamaIndex logo

LlamaIndex

Use Bigml MCP with LlamaIndex

CrewAI logo

CrewAI

Use Bigml MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Bigml MCP server, the agents and LLMs can only access a fixed set of Bigml tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Bigml and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Bigml tools.

Yes, absolutely. You can configure which Bigml scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Bigml data and credentials are handled as safely as possible.

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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