# How to integrate GTmetrix MCP with Pydantic AI ```json { "title": "How to integrate GTmetrix MCP with Pydantic AI", "toolkit": "GTmetrix", "toolkit_slug": "gtmetrix", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/gtmetrix/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/gtmetrix/framework/pydantic-ai.md", "updated_at": "2026-03-29T06:36:47.678Z" } ``` ## Introduction This guide walks you through connecting GTmetrix to Pydantic AI using the Composio tool router. By the end, you'll have a working GTmetrix agent that can run a performance test on your homepage, check latest gtmetrix report for example.com, list top optimization recommendations for your site through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a GTmetrix account through Composio's GTmetrix MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate GTmetrix with - [OpenAI Agents SDK](https://composio.dev/toolkits/gtmetrix/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/gtmetrix/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/gtmetrix/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/gtmetrix/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/gtmetrix/framework/codex) - [OpenClaw](https://composio.dev/toolkits/gtmetrix/framework/openclaw) - [Hermes](https://composio.dev/toolkits/gtmetrix/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/gtmetrix/framework/cli) - [Google ADK](https://composio.dev/toolkits/gtmetrix/framework/google-adk) - [LangChain](https://composio.dev/toolkits/gtmetrix/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/gtmetrix/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/gtmetrix/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/gtmetrix/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/gtmetrix/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 GTmetrix - 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 GTmetrix 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 GTmetrix MCP server, and what's possible with it? The GTmetrix MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your GTmetrix account. It provides structured and secure access so your agent can perform GTmetrix operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `GTMETRIX_DELETE_PAGE` | Delete Page | Tool to delete a specific page in GTmetrix. Use when you need to permanently remove a page resource. | | `GTMETRIX_DELETE_REPORT` | Delete Report | Tool to delete a GTmetrix report. Use when you need to remove an existing performance report from GTmetrix. | | `GTMETRIX_GET_BROWSERS` | Get Browsers | Tool to retrieve the list of available browsers for GTmetrix performance tests. Use when you need to see which browsers are available and their testing capabilities. | | `GTMETRIX_GET_LOCATION` | Get Location Details | Tool to retrieve location details from GTmetrix. Use when you need to get information about a specific GTmetrix test location including name, region, browser support, IP addresses, and access permissions. | | `GTMETRIX_GET_LOCATIONS` | Get Locations | Tool to retrieve the list of available GTmetrix test locations. Use when you need to see which locations are available for testing and their details including supported browsers and access status. | | `GTMETRIX_GET_PAGE_DETAILS` | Get Page Details | Tool to retrieve page details from the user's GTmetrix account. Use when you need to get comprehensive page information including URL, testing configuration, and monitoring frequency. | | `GTMETRIX_GET_PAGE_REPORTS` | Get Page Reports | Tool to retrieve the report list associated with a monitored page in GTmetrix. Use when you need to access historical performance data for a specific page. Supports pagination, sorting, and filtering. | | `GTMETRIX_GET_PAGES` | Get Pages | Tool to retrieve the page list from your GTmetrix account. Returns a paginated collection of monitored pages with their configurations and latest report information. Use when you need to view all monitored pages, check page configurations, or access latest report data. | | `GTMETRIX_GET_REPORT` | Get Report | Tool to retrieve a GTmetrix test report by its identifier. Use when you need to get comprehensive performance metrics, timing data, and links to resources for a specific report. | | `GTMETRIX_GET_SIMULATED_DEVICE` | Get Simulated Device | Tool to retrieve simulated device details. Use when you need information about a specific simulated device including its name, category, manufacturer, user agent, screen dimensions, and pixel ratio. | | `GTMETRIX_GET_SIMULATED_DEVICES` | Get Simulated Devices | Tool to retrieve the list of simulated devices available in GTmetrix. Use when you need to see available device profiles for testing. | | `GTMETRIX_GET_API_ACCOUNT_STATUS` | Get API Account Status | Tool to retrieve the current API account state and remaining credits. Use to check available API credits, refill schedule, and account features. | | `GTMETRIX_GET_TEST_DETAILS` | Get Test Details | Tool to retrieve test details for a specific GTMetrix test. Use when you need to check the status, configuration, or results of a previously initiated test. | | `GTMETRIX_GET_TESTS` | Get Tests | Tool to retrieve the test list from your GTmetrix account with pagination and filtering support. Use when you need to view tests with their state, timestamps, and configuration details. | | `GTMETRIX_RETEST_REPORT` | Retest Report | Tool to initiate a retest of a completed GTmetrix report with same parameters. Use when you need to rerun a test using the exact same analysis parameters as the original test. | | `GTMETRIX_START_TEST` | Start Test | Tool to start a new GTmetrix test for a specified URL. Use when you need to analyze website performance with configurable options like location, browser, and throttling. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The GTmetrix MCP server is an implementation of the Model Context Protocol that connects your AI agent to GTmetrix. It provides structured and secure access so your agent can perform GTmetrix 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 GTmetrix - 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 GTmetrix - MCPServerStreamableHTTP connects to the GTmetrix 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 GTmetrix 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 GTmetrix composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["gtmetrix"], ) 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 GTmetrix endpoint - The agent uses GPT-5 to interpret user commands and perform GTmetrix operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent gtmetrix_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[gtmetrix_mcp], instructions=( "You are a GTmetrix assistant. Use GTmetrix 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 - GTmetrix 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 GTmetrix.\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 GTmetrix composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["gtmetrix"], ) 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 gtmetrix_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[gtmetrix_mcp], instructions=( "You are a GTmetrix assistant. Use GTmetrix 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 GTmetrix.\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 GTmetrix through Composio's Tool Router. With this setup, your agent can perform real GTmetrix 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 + GTmetrix 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 GTmetrix MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/gtmetrix/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/gtmetrix/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/gtmetrix/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/gtmetrix/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/gtmetrix/framework/codex) - [OpenClaw](https://composio.dev/toolkits/gtmetrix/framework/openclaw) - [Hermes](https://composio.dev/toolkits/gtmetrix/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/gtmetrix/framework/cli) - [Google ADK](https://composio.dev/toolkits/gtmetrix/framework/google-adk) - [LangChain](https://composio.dev/toolkits/gtmetrix/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/gtmetrix/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/gtmetrix/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/gtmetrix/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/gtmetrix/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and GTmetrix MCP? With a standalone GTmetrix MCP server, the agents and LLMs can only access a fixed set of GTmetrix tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from GTmetrix 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 GTmetrix tools. ### Can I manage the permissions and scopes for GTmetrix while using Tool Router? Yes, absolutely. You can configure which GTmetrix 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 GTmetrix data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)