# How to integrate Opencage MCP with CrewAI ```json { "title": "How to integrate Opencage MCP with CrewAI", "toolkit": "Opencage", "toolkit_slug": "opencage", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/opencage/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/opencage/framework/crew-ai.md", "updated_at": "2026-05-12T10:20:50.267Z" } ``` ## Introduction This guide walks you through connecting Opencage to CrewAI using the Composio tool router. By the end, you'll have a working Opencage agent that can get coordinates for a hotel address, find the address of given gps coordinates, return geojson for a city location through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Opencage account through Composio's Opencage MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Opencage with - [OpenAI Agents SDK](https://composio.dev/toolkits/opencage/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/opencage/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/opencage/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/opencage/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/opencage/framework/codex) - [OpenClaw](https://composio.dev/toolkits/opencage/framework/openclaw) - [Hermes](https://composio.dev/toolkits/opencage/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/opencage/framework/cli) - [Google ADK](https://composio.dev/toolkits/opencage/framework/google-adk) - [LangChain](https://composio.dev/toolkits/opencage/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/opencage/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/opencage/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/opencage/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Opencage connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Opencage - Build a conversational loop where your agent can execute Opencage 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 Opencage MCP server, and what's possible with it? The Opencage MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Opencage account. It provides structured and secure access to global geocoding services, so your agent can perform actions like translating addresses to coordinates, finding locations from GPS, converting to GeoJSON, and handling multiple output formats on your behalf. - Forward geocoding for addresses: Instantly convert human-readable addresses into precise latitude and longitude coordinates for mapping or logistics tasks. - Reverse geocoding coordinates: Give your agent raw GPS coordinates and receive the nearest human-readable address or location details. - GeoJSON feature generation: Request GeoJSON output for geocoding results, making it easy to visualize or integrate locations in mapping applications. - Flexible output formats: Get geocoding data in XML or JSONP formats, ensuring compatibility with a variety of development workflows and systems. - Seamless integration with open data sources: Tap into comprehensive and up-to-date location information sourced from open datasets for global coverage. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `OPENCAGE_GEOCODE_FORWARD` | Forward Geocode Address | Tool to convert a human-readable address into geographic coordinates. Use when you need to retrieve latitude and longitude from an address. | | `OPENCAGE_GEOCODE_GEOJSON` | Geocode to GeoJSON | Geocode addresses or coordinates and return results in GeoJSON FeatureCollection format. Use this tool when you need: - Geographic data in standard GeoJSON format for mapping applications - Forward geocoding: convert addresses to coordinates - Reverse geocoding: convert coordinates to addresses The response includes coordinates, formatted addresses, and optional annotations like timezone, currency, and sun times for each location. | | `OPENCAGE_GEOCODE_GEOJSONP` | Geocode with JSONP | Geocode an address and return results wrapped in a JavaScript callback function (JSONP format). Use this tool when you need geocoding results that can be directly consumed by JavaScript through a callback function, typically for cross-domain AJAX requests in browser environments. The response wraps standard geocoding JSON with your specified callback function name. Example response: myCallback({"results":[{"geometry":{"lat":52.5,"lng":13.4},"formatted":"Berlin, Germany",...}],...}) | | `OPENCAGE_GEOCODE_GOOGLE_V3_JSON` | Geocode Google v3 JSON | Tool to perform forward geocoding and return results in Google Geocoding API v3 compatible JSON format. Use when you need Google v3 compatible output for legacy integrations. Note: This is a legacy format that may be discontinued; using the standard JSON format is recommended. | | `OPENCAGE_GEOCODE_REVERSE` | Reverse Geocode Coordinates | Tool to convert coordinates to a human-readable address. Use when you have latitude and longitude and need a readable location. | | `OPENCAGE_GEOCODE_XML` | Geocode XML | Geocode a location query and return results in XML format. Supports both forward geocoding (address to coordinates) and reverse geocoding (coordinates to address). Use this when you need XML-formatted output instead of JSON. | | `OPENCAGE_PING_OPENCAGE` | Check API Health | Tool to check API health and connectivity. Returns 'pong' if the API is reachable. Use when you need to verify that the OpenCage API is accessible and operational. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Opencage MCP server is an implementation of the Model Context Protocol that connects your AI agent to Opencage. It provides structured and secure access so your agent can perform Opencage 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 Opencage 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 Opencage 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 Opencage 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 Opencage **What's happening:** - You create a Opencage only session through Composio - Composio returns an MCP HTTP URL that exposes Opencage tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["opencage"]) 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=["opencage"], ) 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 Opencage through Composio's Tool Router. The agent can perform Opencage 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 Opencage MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/opencage/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/opencage/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/opencage/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/opencage/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/opencage/framework/codex) - [OpenClaw](https://composio.dev/toolkits/opencage/framework/openclaw) - [Hermes](https://composio.dev/toolkits/opencage/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/opencage/framework/cli) - [Google ADK](https://composio.dev/toolkits/opencage/framework/google-adk) - [LangChain](https://composio.dev/toolkits/opencage/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/opencage/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/opencage/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/opencage/framework/llama-index) ## 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. 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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 Opencage MCP? With a standalone Opencage MCP server, the agents and LLMs can only access a fixed set of Opencage tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Opencage 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 Opencage tools. ### Can I manage the permissions and scopes for Opencage while using Tool Router? Yes, absolutely. You can configure which Opencage 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 Opencage data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)