# How to integrate Token metrics MCP with Pydantic AI ```json { "title": "How to integrate Token metrics MCP with Pydantic AI", "toolkit": "Token metrics", "toolkit_slug": "token_metrics", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/token_metrics/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/token_metrics/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:28:51.420Z" } ``` ## Introduction This guide walks you through connecting Token metrics to Pydantic AI using the Composio tool router. By the end, you'll have a working Token metrics agent that can show real-time price for ethereum, list top 10 tokens by market cap, get technical indicators for bitcoin hourly through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Token metrics account through Composio's Token metrics MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Token metrics with - [OpenAI Agents SDK](https://composio.dev/toolkits/token_metrics/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/token_metrics/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/token_metrics/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/token_metrics/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/token_metrics/framework/codex) - [OpenClaw](https://composio.dev/toolkits/token_metrics/framework/openclaw) - [Hermes](https://composio.dev/toolkits/token_metrics/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/token_metrics/framework/cli) - [Google ADK](https://composio.dev/toolkits/token_metrics/framework/google-adk) - [LangChain](https://composio.dev/toolkits/token_metrics/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/token_metrics/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/token_metrics/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/token_metrics/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/token_metrics/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 Token metrics - 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 Token metrics 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 Token metrics MCP server, and what's possible with it? The Token metrics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Token Metrics account. It provides structured and secure access to real-time cryptocurrency data and analytics, so your agent can retrieve token prices, analyze market trends, surface technical indicators, and provide actionable trading signals on your behalf. - Real-time price and market data: Instantly get up-to-date prices, trading volumes, and market caps for any supported cryptocurrency, enabling smart portfolio management and trading decisions. - Comprehensive token listings: Fetch a paginated list of supported crypto tokens, including metadata like price, supply, and contract details, to stay on top of the ever-evolving market. - On-demand technical analysis: Retrieve technical indicators for any token and interval, so your agent can offer in-depth charting and analysis to guide investment strategies. - Automated trading signals: Access AI-powered crypto trading entry and exit signals, helping automate or optimize your trading strategies based on actionable insights. - Market cap leaderboards: Easily surface the top cryptocurrencies by market capitalization to monitor market trends, discover new opportunities, or rebalance your holdings. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `TOKEN_METRICS_GET_PRICE` | Get Price | Tool to retrieve real-time price and market metrics for a given cryptocurrency. Use when you need the latest price, volume, and market cap information for trading or analysis. Response fields like `volume24h` or `numberOfHolders` may be absent; treat missing fields as null, not zero. | | `TOKEN_METRICS_GET_TECHNICAL_INDICATORS` | Get Technical Indicators | Tool to retrieve technical indicators for a token. Use when you need technical analysis data for a specific symbol, interval, and indicator. | | `TOKEN_METRICS_GET_TOKENS` | Get Tokens | Tool to retrieve a paginated list of supported tokens with metadata. Use when you need comprehensive token listings across price, market cap, supply, and contract details. Returns token_id values required by TOKEN_METRICS_GET_PRICE and other endpoints — build your token_id mapping here first. Response fields such as volume24h and numberOfHolders may be absent for some tokens; treat missing values as null/unknown, not zero. tokenCreationDate is ISO-8601; convert to UTC for accurate age comparisons. | | `TOKEN_METRICS_GET_TOP_MARKET_CAP_TOKENS` | Get Top Market Cap Tokens | Tool to retrieve a list of tokens ranked by market capitalization. Use when you need an overview of the most valuable cryptocurrencies. | | `TOKEN_METRICS_GET_TRADING_SIGNALS` | Get Trading Signals | Tool to retrieve entry and exit crypto trading signals. Use when optimizing trading strategies with signal-based insights. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Token metrics MCP server is an implementation of the Model Context Protocol that connects your AI agent to Token metrics. It provides structured and secure access so your agent can perform Token metrics 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 Token metrics - 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 Token metrics - MCPServerStreamableHTTP connects to the Token metrics 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 Token metrics 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 Token metrics composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["token_metrics"], ) 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 Token metrics endpoint - The agent uses GPT-5 to interpret user commands and perform Token metrics operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent token_metrics_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[token_metrics_mcp], instructions=( "You are a Token metrics assistant. Use Token metrics 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 - Token metrics 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 Token metrics.\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 Token metrics composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["token_metrics"], ) 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 token_metrics_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[token_metrics_mcp], instructions=( "You are a Token metrics assistant. Use Token metrics 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 Token metrics.\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 Token metrics through Composio's Tool Router. With this setup, your agent can perform real Token metrics 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 + Token metrics 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 Token metrics MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/token_metrics/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/token_metrics/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/token_metrics/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/token_metrics/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/token_metrics/framework/codex) - [OpenClaw](https://composio.dev/toolkits/token_metrics/framework/openclaw) - [Hermes](https://composio.dev/toolkits/token_metrics/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/token_metrics/framework/cli) - [Google ADK](https://composio.dev/toolkits/token_metrics/framework/google-adk) - [LangChain](https://composio.dev/toolkits/token_metrics/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/token_metrics/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/token_metrics/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/token_metrics/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/token_metrics/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Token metrics MCP? With a standalone Token metrics MCP server, the agents and LLMs can only access a fixed set of Token metrics tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Token metrics 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 Token metrics tools. ### Can I manage the permissions and scopes for Token metrics while using Tool Router? Yes, absolutely. You can configure which Token metrics 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 Token metrics data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)