# How to integrate Felt MCP with CrewAI ```json { "title": "How to integrate Felt MCP with CrewAI", "toolkit": "Felt", "toolkit_slug": "felt", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/felt/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/felt/framework/crew-ai.md", "updated_at": "2026-05-12T10:11:13.566Z" } ``` ## Introduction This guide walks you through connecting Felt to CrewAI 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 CrewAI 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) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Felt connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Felt - Build a conversational loop where your agent can execute Felt operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## 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 and API key - A Felt connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 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 **What's happening:** - composio connects your agent to Felt via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Felt MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Felt **What's happening:** - You create a Felt only session through Composio - Composio returns an MCP HTTP URL that exposes Felt tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["felt"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["felt"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Felt through Composio's Tool Router. The agent can perform Felt operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## 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) ## 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 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. 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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 CrewAI? Yes, you can. CrewAI 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)