How to integrate Arize AX MCP with Mastra AI

This guide walks you through connecting Arize AX to Mastra AI using the Composio tool router. By the end, you'll have a working Arize AX agent that can summarize failed spans from latest traces, compare experiment results across prompt versions, create dataset from production traces through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a Arize AX account through Composio's Arize AX MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Arize AX is an AI engineering platform for tracing, evaluating, and improving AI applications. Use it to debug LLM behavior, compare experiments, and improve production AI quality.

24 Tools

Introduction

This guide walks you through connecting Arize AX to Mastra AI using the Composio tool router. By the end, you'll have a working Arize AX agent that can summarize failed spans from latest traces, compare experiment results across prompt versions, create dataset from production traces through natural language commands.

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

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

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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 Arize AX tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Arize AX 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 Arize AX 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 Arize AX MCP server, and what's possible with it?

The Arize AX MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Arize AX account. It provides structured and secure access to your projects, datasets, prompts, experiments, traces, and spans, so your agent can inspect AI activity, manage evaluation data, maintain prompts, run experiments, and review results on your behalf.

  • Project and space discovery: Have your agent find accessible spaces and projects, then retrieve details about the AI applications you monitor.
  • Dataset management: Let the agent create and rename datasets, add or update examples, and review examples from current or named dataset versions.
  • Prompt versioning: Direct your agent to create prompts, publish new immutable versions, update prompt descriptions, and review current or previous versions.
  • Experiment tracking: Instruct your agent to create experiments, append runs, and inspect experiment outputs for evaluation and comparison.
  • Trace and span analysis: Have your agent review bounded sets of traces and spans within chosen time ranges and filters to investigate application behavior.

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 Arize AX 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 Arize AX

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

  const composioMCPUrl = session.mcp.url;
  console.log("Arize AX MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "arize_ax" for Arize AX 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 Arize AX toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "arize_ax-mastra-agent",
    instructions: "You are an AI agent with Arize AX 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: {
        arize_ax: 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 Arize AX 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 Arize AX 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: ["arize_ax"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "arize_ax-mastra-agent",
    instructions: "You are an AI agent with Arize AX 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: { arize_ax: 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 Arize AX 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 Arize AX action and event your agent gets out of the box.

Add Dataset Examples

Append arbitrary-field examples to an Arize dataset or a specified dataset version.

Add Experiment Runs

Append one or more runs to an existing Arize experiment.

Create Dataset

Create an Arize dataset in a space with an explicit initial array of examples.

Create Experiment

Create an Arize experiment with initial runs, associated with exactly one dataset or space.

Create Prompt

Create an Arize prompt and its initial version in a space.

Create Prompt Version

Create a new immutable version of an existing Arize prompt.

Get Dataset

Get one Arize dataset and its versions by dataset ID.

Get Experiment

Get one Arize experiment by its ID, without its experiment runs.

Get Project

Get one accessible Arize project by its ID.

Get Prompt

Get an Arize prompt with its current, explicitly selected, or labeled version.

Get Space

Get one accessible Arize space by its ID.

List Dataset Examples

List one page of examples from an Arize dataset or a specific dataset version, preserving user-defined example fields.

List Datasets

List one page of Arize datasets, optionally filtered by space or dataset name.

List Experiment Runs

List one page of runs and outputs for an Arize experiment, preserving additional run fields.

List Experiments

List one page of Arize experiments, optionally filtered by dataset, space, or name.

List Projects

List one page of accessible Arize projects, optionally filtered by space or project name.

List Prompts

List one page of Arize prompts, optionally filtered by space or prompt name.

List Prompt Versions

List one page of versions for an Arize prompt, newest first.

List Spaces

List one page of Arize spaces accessible to the connected API key, optionally filtered by organization or name.

List Spans

Read one bounded page of spans for an Arize project using optional time and filter constraints.

List Traces

Read one bounded page of traces and nested spans for an Arize project using optional time and filter constraints; nested spans may be truncated per trace.

Update Dataset

Rename an existing Arize dataset.

Update Dataset Examples

Update existing Arize dataset examples by ID, optionally creating a named dataset version.

Update Prompt

Update or clear an Arize prompt's description.

FAQ

Frequently asked questions

With a standalone Arize AX MCP server, the agents and LLMs can only access a fixed set of Arize AX tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Arize AX 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 Arize AX tools.

Yes, absolutely. You can configure which Arize AX 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 Arize AX data and credentials are handled as safely as possible.

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