# How to integrate Perigon MCP with CrewAI ```json { "title": "How to integrate Perigon MCP with CrewAI", "toolkit": "Perigon", "toolkit_slug": "perigon", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/perigon/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/perigon/framework/crew-ai.md", "updated_at": "2026-05-12T10:21:46.737Z" } ``` ## Introduction This guide walks you through connecting Perigon to CrewAI using the Composio tool router. By the end, you'll have a working Perigon agent that can get top technology news headlines today, summarize recent articles about climate change, find news on major stock market events through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Perigon account through Composio's Perigon MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Perigon with - [OpenAI Agents SDK](https://composio.dev/toolkits/perigon/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/perigon/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/perigon/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/perigon/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/perigon/framework/codex) - [OpenClaw](https://composio.dev/toolkits/perigon/framework/openclaw) - [Hermes](https://composio.dev/toolkits/perigon/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/perigon/framework/cli) - [Google ADK](https://composio.dev/toolkits/perigon/framework/google-adk) - [LangChain](https://composio.dev/toolkits/perigon/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/perigon/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/perigon/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/perigon/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Perigon connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Perigon - Build a conversational loop where your agent can execute Perigon 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 Perigon MCP server, and what's possible with it? The Perigon MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Perigon account. It provides structured and secure access to real-time news and web content data, so your agent can perform actions like searching news articles, aggregating trending stories, extracting web data, and analyzing news sentiment on your behalf. - Comprehensive news article search: Empower your agent to search and retrieve news articles from global sources using filters like date, topic, publisher, or region. - Real-time trending stories aggregation: Automatically gather and summarize the latest trending news across categories such as politics, technology, finance, and more. - Web content extraction: Let your agent pull structured data from online articles and websites, making it easy to analyze or repurpose content. - News sentiment and topic analysis: Enable your agent to analyze the sentiment and topical coverage of news stories to provide actionable insights or reports. - Customized news monitoring: Set up continuous monitoring for specific keywords, companies, or industries, so your agent can keep you updated with relevant news as it happens. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PERIGON_GET_ARTICLES` | Get News Articles | Tool to retrieve a list of news articles based on filters. Use when keywords, sources, or date ranges are specified. | | `PERIGON_GET_COMPANIES` | Get Companies | Tool to retrieve information on companies in Perigon’s entity database. Use when you need a full list of companies. Use after confirming a valid API key is present. | | `PERIGON_GET_JOURNALISTS` | Get Journalists | Tool to retrieve journalist profiles including title, Twitter handle, bio, and location. Use when you need detailed journalist info to enrich content with author metadata. | | `PERIGON_GET_SOURCES` | Get Media Sources | Tool to retrieve a list of media sources with filtering options. Use when you need to list sources by domain, country, category, or traffic metrics. | | `PERIGON_GET_STORIES` | Get Stories | Tool to retrieve clusters of related articles covering the same event or topic with aggregate metrics. Use when you need to fetch filtered and sorted story clusters after configuring query parameters. | | `PERIGON_GET_TOPICS` | Get Topics | Tool to retrieve all available Perigon news topics. Returns a list of topics that can be used to filter articles or stories. Each topic includes an ID, name, and labels (category/subcategory). | | `PERIGON_GET_WIKIPEDIA` | Get Wikipedia Articles | Tool to search and filter Wikipedia pages. Use when you have a search query ready and want to retrieve relevant Wikipedia articles. | | `PERIGON_VECTOR_SEARCH_ARTICLES` | Vector Search Articles | Tool to perform a vector search on Perigon’s real-time news database. Use when you need to retrieve semantically similar news articles given a natural language query. | | `PERIGON_VECTOR_SEARCH_WIKIPEDIA` | Vector Search Wikipedia | Tool to perform semantic retrieval of Wikipedia pages using vector search. Use after obtaining a search query to find relevant Wikipedia articles. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Perigon MCP server is an implementation of the Model Context Protocol that connects your AI agent to Perigon. It provides structured and secure access so your agent can perform Perigon 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 Perigon 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 Perigon 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 Perigon 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 Perigon **What's happening:** - You create a Perigon only session through Composio - Composio returns an MCP HTTP URL that exposes Perigon tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["perigon"]) 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=["perigon"], ) 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 Perigon through Composio's Tool Router. The agent can perform Perigon 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 Perigon MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/perigon/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/perigon/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/perigon/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/perigon/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/perigon/framework/codex) - [OpenClaw](https://composio.dev/toolkits/perigon/framework/openclaw) - [Hermes](https://composio.dev/toolkits/perigon/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/perigon/framework/cli) - [Google ADK](https://composio.dev/toolkits/perigon/framework/google-adk) - [LangChain](https://composio.dev/toolkits/perigon/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/perigon/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/perigon/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/perigon/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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You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Perigon tools. ### Can I manage the permissions and scopes for Perigon while using Tool Router? Yes, absolutely. You can configure which Perigon 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 Perigon data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)