# How to integrate Artificial Analysis MCP with Mastra AI

```json
{
  "title": "How to integrate Artificial Analysis MCP with Mastra AI",
  "toolkit": "Artificial Analysis",
  "toolkit_slug": "artificial_analysis",
  "framework": "Mastra AI",
  "framework_slug": "mastra-ai",
  "url": "https://composio.dev/toolkits/artificial_analysis/framework/mastra-ai",
  "markdown_url": "https://composio.dev/toolkits/artificial_analysis/framework/mastra-ai.md",
  "updated_at": "2026-07-28T11:13:25.321Z"
}
```

## Introduction

This guide walks you through connecting Artificial Analysis to Mastra AI using the Composio tool router. By the end, you'll have a working Artificial Analysis agent that can compare model pricing and latency, rank providers by coding benchmark scores, find cheapest high-intelligence language models through natural language commands.
This guide will help you understand how to give your Mastra AI agent real control over a Artificial Analysis account through Composio's Artificial Analysis MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Artificial Analysis with

- [OpenAI Agents SDK](https://composio.dev/toolkits/artificial_analysis/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/artificial_analysis/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/artificial_analysis/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/artificial_analysis/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/artificial_analysis/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/artificial_analysis/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/artificial_analysis/framework/hermes-agent)
- [Google ADK](https://composio.dev/toolkits/artificial_analysis/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/artificial_analysis/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/artificial_analysis/framework/ai-sdk)
- [LlamaIndex](https://composio.dev/toolkits/artificial_analysis/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/artificial_analysis/framework/crew-ai)
- [Pydantic AI](https://composio.dev/toolkits/artificial_analysis/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/artificial_analysis/framework/autogen)

## 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 Artificial Analysis tools
- Connect Mastra's MCP client to the Composio generated MCP URL
- Fetch Artificial Analysis 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 Artificial Analysis 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 Artificial Analysis MCP server, and what's possible with it?

The Artificial Analysis MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Artificial Analysis account. It provides structured and secure access so your agent can perform Artificial Analysis operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ARTIFICIAL_ANALYSIS_LIST_IMAGE_EDITING_MODELS` | List Image-Editing Models (Arena) | List image-editing models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval. Use to find the best-rated image-editing model. Returns all models in one call (unpaginated). |
| `ARTIFICIAL_ANALYSIS_LIST_IMAGE_TO_VIDEO_AUDIO_MODELS` | List Image-to-Video-with-Audio Models (Arena) | List image-to-video-with-audio models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval. Returns all models unpaginated in one call. No legacy sibling exists for this modality. |
| `ARTIFICIAL_ANALYSIS_LIST_IMAGE_TO_VIDEO_MODELS` | List Image-to-Video Models (Arena) | List image-to-video models ranked by the Artificial Analysis image-to-video arena, with Elo score and 95% confidence interval. Returns all models unpaginated in one call. |
| `ARTIFICIAL_ANALYSIS_LIST_LANGUAGE_MODELS` | List Language Models (Intelligence & Pricing) | List LLMs from the Artificial Analysis leaderboard with intelligence, coding and agentic indices, pricing, and performance (speed and latency). Pass the response's next_cursor back via `cursor` to fetch the next page; pass `start`/`end` to return just a slice of a page (e.g. start=0, end=25 for the top 25). |
| `ARTIFICIAL_ANALYSIS_LIST_MUSIC_INSTRUMENTAL_MODELS` | List Instrumental Music Models (Arena) | List instrumental music-generation models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval. Returns all models in one call (unpaginated). Note: music items have no slug field. |
| `ARTIFICIAL_ANALYSIS_LIST_MUSIC_WITH_VOCALS_MODELS` | List Music-with-Vocals Models (Arena) | List music-with-vocals generation models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval. Use to find the best-rated music-with-vocals model. Returns all models in one call (unpaginated). |
| `ARTIFICIAL_ANALYSIS_LIST_SPEECH_TO_SPEECH_MODELS` | List Speech-to-Speech Models | List speech-to-speech models with Big Bench Audio (bba_score), Full Duplex Bench (fdb_score), and Tau-Voice (tau_voice_score) quality scores. Tau-Voice uses each model's best result across providers. Use to compare voice-to-voice model quality. Returns all models in one call (unpaginated). |
| `ARTIFICIAL_ANALYSIS_LIST_SPEECH_TO_TEXT_MODELS` | List Speech-to-Text Models (WER) | List speech-to-text (transcription) models with the Artificial Analysis overall word-error-rate index (aa_wer_index). Lower is better. Use to compare transcription accuracy across models. Returns all models in one call (unpaginated). |
| `ARTIFICIAL_ANALYSIS_LIST_TEXT_TO_IMAGE_MODELS` | List Text-to-Image Models (Arena) | List text-to-image models ranked by the Artificial Analysis image arena, with Elo score and 95% confidence interval. Use to find the best-rated text-to-image model. Returns all models in one call (unpaginated). |
| `ARTIFICIAL_ANALYSIS_LIST_TEXT_TO_SPEECH_MODELS` | List Text-to-Speech Models (Arena) | List text-to-speech (TTS) models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval. Use to find the best-rated text-to-speech model. Returns all models in one call (unpaginated). |
| `ARTIFICIAL_ANALYSIS_LIST_TEXT_TO_VIDEO_AUDIO_MODELS` | List Text-to-Video-with-Audio Models (Arena) | List text-to-video-with-audio models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval. No legacy sibling exists for this modality. |
| `ARTIFICIAL_ANALYSIS_LIST_TEXT_TO_VIDEO_MODELS` | List Text-to-Video Models (Arena) | List text-to-video models ranked by the Artificial Analysis arena, with Elo score and 95% confidence interval. Use to find the best-rated text-to-video model. Returns all models in one call (unpaginated). |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Artificial Analysis MCP server is an implementation of the Model Context Protocol that connects your AI agent to Artificial Analysis. It provides structured and secure access so your agent can perform Artificial Analysis operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.
- This key lets your Mastra agent talk to Composio and reach Artificial Analysis through MCP.

### 2. Install dependencies

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

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import libraries and validate environment

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
```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,
});
```

### 5. Create a Tool Router session for Artificial Analysis

What's happening:
- create spins up a short-lived MCP HTTP endpoint for this user
- The toolkits array contains "artificial_analysis" for Artificial Analysis access
- session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
```typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["artificial_analysis"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Artificial Analysis MCP URL:", composioMCPUrl);
```

### 6. Configure Mastra MCP client and fetch tools

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 Artificial Analysis toolkit
```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);
```

### 7. Create the Mastra agent

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

### 8. Set up interactive chat interface

What's happening:
- messages keeps the full conversation history in Mastra's expected format
- agent.generate runs the agent with conversation history and Artificial Analysis 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
```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: {
        artificial_analysis: 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);
});
```

## Complete Code

```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: ["artificial_analysis"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "artificial_analysis-mastra-agent",
    instructions: "You are an AI agent with Artificial Analysis 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: { artificial_analysis: 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 Artificial Analysis 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

## How to build Artificial Analysis MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/artificial_analysis/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/artificial_analysis/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/artificial_analysis/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/artificial_analysis/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/artificial_analysis/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/artificial_analysis/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/artificial_analysis/framework/hermes-agent)
- [Google ADK](https://composio.dev/toolkits/artificial_analysis/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/artificial_analysis/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/artificial_analysis/framework/ai-sdk)
- [LlamaIndex](https://composio.dev/toolkits/artificial_analysis/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/artificial_analysis/framework/crew-ai)
- [Pydantic AI](https://composio.dev/toolkits/artificial_analysis/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/artificial_analysis/framework/autogen)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - 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.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Artificial Analysis MCP?

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

### Can I use Tool Router MCP with Mastra AI?

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 Artificial Analysis tools.

### Can I manage the permissions and scopes for Artificial Analysis while using Tool Router?

Yes, absolutely. You can configure which Artificial Analysis 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.

### How safe is my data with Composio Tool Router?

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 Artificial Analysis data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)