# How to integrate Bitquery MCP with Pydantic AI ```json { "title": "How to integrate Bitquery MCP with Pydantic AI", "toolkit": "Bitquery", "toolkit_slug": "bitquery", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/bitquery/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/bitquery/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:03:11.370Z" } ``` ## Introduction This guide walks you through connecting Bitquery to Pydantic 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 through natural language commands. This guide will help you understand how to give your Pydantic 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. ## Also integrate Bitquery with - [OpenAI Agents SDK](https://composio.dev/toolkits/bitquery/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/bitquery/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/bitquery/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/bitquery/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/bitquery/framework/codex) - [OpenClaw](https://composio.dev/toolkits/bitquery/framework/openclaw) - [Hermes](https://composio.dev/toolkits/bitquery/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/bitquery/framework/cli) - [Google ADK](https://composio.dev/toolkits/bitquery/framework/google-adk) - [LangChain](https://composio.dev/toolkits/bitquery/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/bitquery/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/bitquery/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/bitquery/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/bitquery/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Bitquery - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Bitquery workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## 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 | Tool slug | Name | Description | |---|---|---| | `BITQUERY_ARCHIVE_DATABASE_QUERY` | Archive Database Query | Query the Bitquery Archive Database (V1 API) for historical blockchain data. The Archive Database provides complete historical blockchain data across 40+ blockchains including Bitcoin, Ethereum, BSC, Solana, and more. Data has a delay of tens of minutes to hours from real-time. For near-real-time data, use the Realtime Database Query instead. The V1 API uses blockchain-specific root types (bitcoin, ethereum, etc.) with fields like blocks, transactions, transfers, and trades. Queries support filtering, pagination with limit/offset, and sorting with orderBy. Example queries: - Bitcoin blocks: { bitcoin { blocks(limit: 5, orderBy: {descending: height}) { height } } } - Ethereum transactions: { ethereum { transactions(limit: 10) { hash value } } } | | `BITQUERY_COMBINED_DATABASE_QUERY` | Combined Database Query | Query Bitquery's Combined Database (v2 API) for blockchain data across 40+ networks. Use this tool to fetch real-time and historical blockchain data including: - Blocks, transactions, and events - Token transfers and balances - DEX trades and liquidity data - Smart contract interactions - NFT data and metadata Supported networks include: Ethereum (eth), BSC (bsc), Polygon (matic), Solana, Tron, and more. The v2 API uses a different schema than v1 - use EVM(network: eth) instead of ethereum root field. | | `BITQUERY_CONDITIONAL_METRICS` | Conditional Metrics Snippet | Generate a Bitquery GraphQL metric snippet with conditional logic using the 'if:' attribute. This tool builds metric aggregation snippets (count, sum, avg, min, max) that can be embedded in Bitquery GraphQL queries. The 'if:' filter allows applying conditions directly to metric calculations, enabling conditional aggregation like counting only successful transactions. Output format examples: - count(if: {Block: {GasUsed: {gt: "0"}}}) - sum(of: Block_GasUsed if: {Block: {Time: {after: "2024-01-01"}}}) - myAlias: avg(of: Transaction_Value if: {Transaction: {Success: true}}) | | `BITQUERY_DATABASE_SELECTION` | Database Selection | Tool to select the database (archive, realtime, combined) to query at the top level of a GraphQL request. Use after determining whether you need live, historical, or combined blockchain data. | | `BITQUERY_EARLY_ACCESS_PROGRAM_QUERY` | Early Access Program Query | Execute GraphQL queries against the Bitquery Early Access Program (EAP) Streaming API. This tool queries the EAP endpoint (streaming.bitquery.io/eap) for real-time blockchain data. The EAP provides access to streaming data across various blockchain networks including Solana, EVM chains (Ethereum, Polygon, etc.), and others for evaluation purposes. Key features: - Real-time blockchain data with minimal latency - Supports both queries and subscriptions - Networks: Solana, Ethereum, Polygon (Matic), and other EVM-compatible chains Note: EAP is limited to real-time data only. For historical data, use the Archive Database Query. Existing users can continue using EAP; new users should prefer the V2 endpoint for most use cases. Example queries: - Get latest ETH blocks: { EVM(network: eth) { Blocks(limit: {count: 5}) { Block { Number Time } } } } - Solana DEX trades: subscription { Solana { DEXTrades { Block { Time } Trade { Price } } } } | | `BITQUERY_NETWORK_SELECTION` | Network Selection | Tool to select the blockchain network for GraphQL queries. Use before constructing dataset or metric queries to ensure the correct chain is targeted. | | `BITQUERY_OPTIONS_QUERY` | Options Query | Tool to fetch GraphQL dataset options via schema introspection. Use when you need to discover root-level query fields and their arguments before building queries. Dataset and token availability varies by Bitquery environment; verify available fields here before constructing complex queries that depend on specific datasets. | | `BITQUERY_PRICE_ASYMMETRY_METRIC` | Price Asymmetry Metric | Tool to generate GraphQL PriceAsymmetry filter snippet. Use when you need to filter trades based on price asymmetry metric. | | `BITQUERY_REALTIME_DATABASE_QUERY` | Realtime Database Query | Query the Bitquery Streaming (V2) API for realtime blockchain data. This tool accesses the Bitquery Streaming API at streaming.bitquery.io/graphql which provides real-time blockchain data with minimal latency. Use this for recent data (within minutes). For historical data, use the Archive Database Query. Supported query formats: - V2 EVM queries: { EVM(network: eth) { Blocks(limit: {count: 5}) { Block { Number Time } } } } - V2 Bitcoin queries: { bitcoin(network: bitcoin) { blocks(limit: {count: 5}) { height timestamp { time } } } } Note: Requires an active Bitquery subscription for streaming API access. | | `BITQUERY_SELECT_BY_METRIC` | Select By Metric | Tool to generate a GraphQL metric snippet filtering by its value using selectWhere. Use when you need to include only metrics meeting specific value conditions (e.g., only positive sums). | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Bitquery MCP server is an implementation of the Model Context Protocol that connects your AI agent to Bitquery. It provides structured and secure access so your agent can perform Bitquery 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: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 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'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Bitquery - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Bitquery - MCPServerStreamableHTTP connects to the Bitquery MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Bitquery tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Bitquery composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["bitquery"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Bitquery endpoint - The agent uses GPT-5 to interpret user commands and perform Bitquery operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent bitquery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[bitquery_mcp], instructions=( "You are a Bitquery assistant. Use Bitquery tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Bitquery API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Bitquery.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Bitquery composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["bitquery"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent bitquery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[bitquery_mcp], instructions=( "You are a Bitquery assistant. Use Bitquery tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Bitquery.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Bitquery through Composio's Tool Router. With this setup, your agent can perform real Bitquery actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Bitquery for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## How to build Bitquery MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/bitquery/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/bitquery/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/bitquery/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/bitquery/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/bitquery/framework/codex) - [OpenClaw](https://composio.dev/toolkits/bitquery/framework/openclaw) - [Hermes](https://composio.dev/toolkits/bitquery/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/bitquery/framework/cli) - [Google ADK](https://composio.dev/toolkits/bitquery/framework/google-adk) - [LangChain](https://composio.dev/toolkits/bitquery/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/bitquery/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/bitquery/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/bitquery/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/bitquery/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management. - [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters. - [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations. - [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions. - [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows. - [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations. - [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements. - [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints. - [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots. - [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches. - [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand. - [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance. - [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows. - [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. - [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### 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 Pydantic AI? Yes, you can. Pydantic 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)