How to integrate Bitquery MCP with Mastra AI

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Introduction

This guide walks you through connecting Bitquery to Mastra AI using the Composio tool router. By the end, you'll have a working Bitquery agent that can show real-time ethereum mempool transactions, count unique wallet addresses for solana, query historical bitcoin transactions from 2021, aggregate all erc20 transfers on polygon today through natural language commands.

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

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

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

The Bitquery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bitquery account. It provides structured and secure access to blockchain datasets and real-time analytics, so your agent can perform actions like querying historical transactions, streaming mempool activity, selecting blockchain networks, and aggregating metrics across 40+ supported chains.

  • Seamless blockchain data querying: Let your agent run powerful queries on historical or real-time blockchain data across multiple networks using Bitquery's combined or archive databases.
  • Live mempool monitoring: Subscribe and stream pending transactions from EVM-compatible chains in real time, enabling instant insights into network activity as it happens.
  • On-demand network and database selection: Have your agent dynamically select blockchain networks and datasets—like Ethereum, BNB Chain, or others—to tailor queries for your specific use case.
  • Metric aggregation and analysis: Automate the aggregation of transaction counts, unique values, or conditional metrics, empowering your agent to analyze blockchain trends without manual intervention.
  • Advanced GraphQL customization: Use aliases and conditional snippets to refine data responses, ensuring clarity and precise control in complex blockchain analytics workflows.

Supported Tools & Triggers

Tools
Aliases MetricTool to use graphql aliases to rename fields in the response for clarity and disambiguation.
Archive Database QueryTool to query the archive database.
Combined Database QueryTool to query the combined database, which merges archive and real-time databases.
Conditional Metrics SnippetTool to generate a graphql metric snippet with conditional logic.
Count Distinct MetricTool to use the count distinct metric to aggregate unique values for a field.
Count MetricTool to use the count metric to aggregate the number of records matching a graphql query.
Database SelectionTool to select the database (archive, realtime, combined) to query at the top level of a graphql request.
Early Access Program QueryTool to access streaming data across various blockchain networks for evaluation purposes.
Mempool SubscriptionTool to subscribe to real-time mempool updates for evm chains (ethereum, bnb, etc.
Network SelectionTool to select the blockchain network for graphql queries.
Options QueryTool to fetch graphql dataset options via schema introspection.
Price Asymmetry MetricTool to generate graphql priceasymmetry filter snippet.
Quantile MetricTool to calculate quantiles to understand the distribution of numerical data.
Realtime Database QueryRealtime database query
Select By MetricTool to generate a graphql metric snippet filtering by its value using selectwhere.
Statistics MetricTool to compute statistical metrics (mean, median, etc.
Sum MetricTool to calculate the sum of a specified field's values across defined dimensions.
Uniq MetricTool to estimate the count of unique values using the uniq metric.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

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

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 Bitquery through MCP.

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

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

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

Create a Tool Router session for Bitquery

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

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

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 Bitquery toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "bitquery-mastra-agent",
    instructions: "You are an AI agent with Bitquery 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

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: {
        bitquery: 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 Bitquery 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 Bitquery 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: ["bitquery"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "bitquery-mastra-agent",
    instructions: "You are an AI agent with Bitquery 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: { bitquery: 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 Bitquery 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 Bitquery MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Bitquery MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Bitquery MCP with Claude Agent SDK

Google ADK

Google ADK

Use Bitquery MCP with Google ADK

LangChain

LangChain

Use Bitquery MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Bitquery MCP with Vercel AI SDK

LlamaIndex

LlamaIndex

Use Bitquery MCP with LlamaIndex

CrewAI

CrewAI

Use Bitquery MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Bitquery MCP?

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

Can I manage the permissions and scopes for Bitquery while using Tool Router?

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

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