# How to integrate Parsera MCP with CrewAI ```json { "title": "How to integrate Parsera MCP with CrewAI", "toolkit": "Parsera", "toolkit_slug": "parsera", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/parsera/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/parsera/framework/crew-ai.md", "updated_at": "2026-05-12T10:21:24.069Z" } ``` ## Introduction This guide walks you through connecting Parsera to CrewAI using the Composio tool router. By the end, you'll have a working Parsera agent that can extract all headers from this blog post, get product names and prices from a webpage, pull all links from a news article through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Parsera account through Composio's Parsera MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Parsera with - [OpenAI Agents SDK](https://composio.dev/toolkits/parsera/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/parsera/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/parsera/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/parsera/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/parsera/framework/codex) - [OpenClaw](https://composio.dev/toolkits/parsera/framework/openclaw) - [Hermes](https://composio.dev/toolkits/parsera/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/parsera/framework/cli) - [Google ADK](https://composio.dev/toolkits/parsera/framework/google-adk) - [LangChain](https://composio.dev/toolkits/parsera/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/parsera/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/parsera/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/parsera/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Parsera connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Parsera - Build a conversational loop where your agent can execute Parsera 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 Parsera MCP server, and what's possible with it? The Parsera MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Parsera account. It provides structured and secure access to website content, so your agent can extract markdown, parse structured data, and automate web scraping workflows on your behalf. - Extract markdown from webpages: Ask your agent to pull clean, readable markdown content directly from any public URL or file. - Parse and structure HTML content: Let your agent analyze raw HTML or text and convert it into structured data you can use for downstream tasks. - Automate data extraction workflows: Direct your agent to combine fetching, parsing, and formatting steps to streamline web scraping projects end-to-end. - Simplify report and content generation: Have your agent turn scraped website data into markdown reports or ready-to-use documentation for your projects. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PARSERA_CREATE_SCRAPER` | Create Scraper | Tool to create a new empty scraper for your account. Returns a scraper_id that can be used with the generate endpoint to generate scraping code. | | `PARSERA_DELETE_SCRAPER` | Delete Scraper | Tool to delete an existing scraper by its ID. Use when you need to remove a scraper that was created through the /v1/scrapers/new endpoint. | | `PARSERA_EXTRACT_DATA` | Extract Data from Webpage | Tool to perform LLM-powered data extraction from a live webpage URL with specified attributes. Use when you need to extract structured data from web pages based on field descriptions. | | `PARSERA_EXTRACT_MARKDOWN` | Extract Markdown | Tool to extract markdown content from a file or URL. | | `PARSERA_GET_LLM_SPECS` | Get LLM Specifications | Tool to retrieve standardized LLM capabilities and pricing specifications. Use to get up-to-date information about models from various providers. | | `PARSERA_GET_PROXY_COUNTRIES` | Get Proxy Countries | Tool to retrieve the list of available proxy countries for web scraping requests. Use when you need to know which countries are supported for proxy-based scraping. | | `PARSERA_HEALTH_CHECK` | Health Check | Tool to verify API availability and operational status. Use to check if the Parsera service is accessible before making other API calls. | | `PARSERA_LIST_AGENTS` | List Agents | Tool to retrieve all available agents for the authenticated user. Use when you need to list agents that can be used for scraping tasks. | | `PARSERA_LIST_SCRAPERS` | List Scrapers | Tool to list all templates and old scrapers for the authenticated user. Use when you need to retrieve available scraper configurations. | | `PARSERA_PARSE_CONTENT2` | Parse Content (Enhanced) | Tool to extract structured data from raw HTML or text content using AI with advanced options. Use when you have content already loaded and need to extract specific fields with pagination or different extraction modes. | | `PARSERA_REMOVE_AGENT` | Remove Agent | Tool to delete an existing agent by name. Use when you need to remove a previously created agent from the Parsera platform. | | `PARSERA_RUN_SCRAPER_TEMPLATE` | Run Scraper Template | Tool to run a scraper template on a specified URL with optional proxy and cookies. Use when you need to execute a pre-defined scraper template to extract structured data from web pages. | | `PARSERA_SCRAPE_WITH_AGENT` | Scrape With Agent | Tool to run a previously generated scraper agent on a specific URL to extract structured data. Use when you need to apply an existing scraper to a webpage. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Parsera MCP server is an implementation of the Model Context Protocol that connects your AI agent to Parsera. It provides structured and secure access so your agent can perform Parsera 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 Parsera 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 Parsera 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 Parsera 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 Parsera **What's happening:** - You create a Parsera only session through Composio - Composio returns an MCP HTTP URL that exposes Parsera tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["parsera"]) 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=["parsera"], ) 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 Parsera through Composio's Tool Router. The agent can perform Parsera 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 Parsera MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/parsera/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/parsera/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/parsera/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/parsera/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/parsera/framework/codex) - [OpenClaw](https://composio.dev/toolkits/parsera/framework/openclaw) - [Hermes](https://composio.dev/toolkits/parsera/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/parsera/framework/cli) - [Google ADK](https://composio.dev/toolkits/parsera/framework/google-adk) - [LangChain](https://composio.dev/toolkits/parsera/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/parsera/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/parsera/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/parsera/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 enables structured web interaction and data extraction for smarter automations. - [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. 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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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Parsera MCP? With a standalone Parsera MCP server, the agents and LLMs can only access a fixed set of Parsera tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Parsera 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 Parsera tools. ### Can I manage the permissions and scopes for Parsera while using Tool Router? Yes, absolutely. You can configure which Parsera 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 Parsera data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)