# How to integrate Felt MCP with Pydantic AI ```json { "title": "How to integrate Felt MCP with Pydantic AI", "toolkit": "Felt", "toolkit_slug": "felt", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/felt/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/felt/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:11:13.566Z" } ``` ## Introduction This guide walks you through connecting Felt to Pydantic AI using the Composio tool router. By the end, you'll have a working Felt agent that can add geojson features to an existing map, duplicate a project map for a new client, delete a layer from your city zoning map through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Felt account through Composio's Felt MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Felt with - [OpenAI Agents SDK](https://composio.dev/toolkits/felt/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/felt/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/felt/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/felt/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/felt/framework/codex) - [OpenClaw](https://composio.dev/toolkits/felt/framework/openclaw) - [Hermes](https://composio.dev/toolkits/felt/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/felt/framework/cli) - [Google ADK](https://composio.dev/toolkits/felt/framework/google-adk) - [LangChain](https://composio.dev/toolkits/felt/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/felt/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/felt/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/felt/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/felt/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 Felt - 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 Felt 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 Felt MCP server, and what's possible with it? The Felt MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Felt account. It provides structured and secure access to your maps, projects, and geospatial data, so your agent can perform actions like creating projects, modifying maps, updating map elements, and retrieving user or map details on your behalf. - Project and map creation: Instantly have your agent create new Felt projects and initialize interactive maps to kickstart geospatial workflows. - Element and layer management: Direct your agent to add, update, or delete map elements and layers—making it easy to modify map content or clean up unwanted data. - Map duplication and deletion: Clone existing maps for experimentation or backup, or remove entire maps and projects when they’re no longer needed. - Detailed map and user insights: Retrieve comprehensive details about any specific map or your authenticated user profile for streamlined map management and reporting. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `FELT_CREATE_OR_UPDATE_ELEMENTS` | Create or Update Elements | Create or update map elements using GeoJSON FeatureCollection format. Creates new elements by default; to update existing elements, include 'felt:id' in the feature's properties. Supports Point, LineString, Polygon, and Multi-type geometries. Returns the created/updated elements with assigned IDs and Felt-specific properties. | | `FELT_CREATE_PROJECT` | Create Project | Create a new Felt project with the specified name and visibility settings. Projects are organizational containers for grouping related maps within a workspace. | | `FELT_DELETE_ELEMENT` | Delete Element | Tool to delete a specific element from a map. Use when you have both map and element IDs and need to remove the element permanently. | | `FELT_DELETE_LAYER` | Delete Layer | Tool to delete a specific layer from a map. Use when you have the map's and layer's IDs and need to remove it permanently. | | `FELT_DELETE_MAP` | Delete Map | Permanently deletes a map and all its associated data from Felt. WARNING: This action cannot be undone. The map and all its layers, elements, and comments will be permanently removed. Use when you have the map's ID and need to permanently remove it. Returns no content (HTTP 204) on success. | | `FELT_DELETE_PROJECT` | Delete Project | Tool to delete a project and all its contents. Use when you need to permanently remove a project after confirmation. | | `FELT_DUPLICATE_MAP` | Duplicate Map | Creates a complete copy of a Felt map including all layers, elements, and configuration. Use when you need to clone an existing map to a new location or create a template-based map. The duplicated map can optionally be placed in a specific project or folder. | | `FELT_GET_MAP_DETAILS` | Get Map Details | Retrieves comprehensive details of a specific Felt map including title, URL, layers, elements, basemap settings, access permissions, and timestamps. Requires a valid map ID. Use this when you need to: - Get complete map configuration and metadata - Access map layers and elements - Check map permissions and access settings - Retrieve map URLs for sharing | | `FELT_GET_USER_DETAILS` | Get User Details | Tool to retrieve information about the authenticated user. Use after obtaining a valid token to fetch user profile details. | | `FELT_LIST_ELEMENT_GROUPS` | List Element Groups | Retrieves all element groups from a Felt map. Element groups are collections of geographic features (points, lines, polygons) organized together. Each group returns a GeoJSON FeatureCollection with the group's elements, along with styling properties like color and symbol. Use this when you need to discover what element groups exist on a map or access grouped geographic data. | | `FELT_LIST_ELEMENTS` | List Elements | Lists all elements on a specific map as a GeoJSON FeatureCollection. Returns elements that are not in element groups. Use when you need to retrieve the map's direct elements after obtaining a valid map_id. | | `FELT_LIST_LAYERS` | List Layers | Tool to list all layers on a specific map. Returns all layers present on the map with their complete metadata including status, geometry type, styling, and attributes. Use this when you need to inspect or enumerate the data layers on a map. | | `FELT_LIST_PROJECTS` | List Projects | Tool to retrieve a list of projects accessible to the user. Use when you need to browse or select from existing projects before proceeding. | | `FELT_LIST_SOURCES` | List Sources | List all data sources (external data connections) accessible to the authenticated user. Sources represent connections to external data providers like BigQuery, PostgreSQL, S3, Snowflake, etc. Use this to discover available sources before importing data from them into Felt maps. Each source includes sync status, connection type, and access permissions. | | `FELT_UPDATE_PROJECT` | Update Project | Tool to update an existing project's name or visibility. Use after confirming the project_id. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Felt MCP server is an implementation of the Model Context Protocol that connects your AI agent to Felt. It provides structured and secure access so your agent can perform Felt 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 Felt - 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 Felt - MCPServerStreamableHTTP connects to the Felt 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 Felt 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 Felt composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["felt"], ) 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 Felt endpoint - The agent uses GPT-5 to interpret user commands and perform Felt operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent felt_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[felt_mcp], instructions=( "You are a Felt assistant. Use Felt 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 - Felt 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 Felt.\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 Felt composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["felt"], ) 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 felt_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[felt_mcp], instructions=( "You are a Felt assistant. Use Felt 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 Felt.\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 Felt through Composio's Tool Router. With this setup, your agent can perform real Felt 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 + Felt 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 Felt MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/felt/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/felt/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/felt/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/felt/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/felt/framework/codex) - [OpenClaw](https://composio.dev/toolkits/felt/framework/openclaw) - [Hermes](https://composio.dev/toolkits/felt/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/felt/framework/cli) - [Google ADK](https://composio.dev/toolkits/felt/framework/google-adk) - [LangChain](https://composio.dev/toolkits/felt/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/felt/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/felt/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/felt/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/felt/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. - [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. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [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 Felt MCP? With a standalone Felt MCP server, the agents and LLMs can only access a fixed set of Felt tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Felt 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 Felt tools. ### Can I manage the permissions and scopes for Felt while using Tool Router? Yes, absolutely. You can configure which Felt 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 Felt data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)