# How to integrate Linkhut MCP with CrewAI ```json { "title": "How to integrate Linkhut MCP with CrewAI", "toolkit": "Linkhut", "toolkit_slug": "linkhut", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/linkhut/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/linkhut/framework/crew-ai.md", "updated_at": "2026-05-12T10:17:42.375Z" } ``` ## Introduction This guide walks you through connecting Linkhut to CrewAI using the Composio tool router. By the end, you'll have a working Linkhut agent that can add this article as a private bookmark, list all bookmarks tagged with 'research', update the note on your saved github link through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Linkhut account through Composio's Linkhut MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Linkhut with - [OpenAI Agents SDK](https://composio.dev/toolkits/linkhut/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/linkhut/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/linkhut/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/linkhut/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/linkhut/framework/codex) - [OpenClaw](https://composio.dev/toolkits/linkhut/framework/openclaw) - [Hermes](https://composio.dev/toolkits/linkhut/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/linkhut/framework/cli) - [Google ADK](https://composio.dev/toolkits/linkhut/framework/google-adk) - [LangChain](https://composio.dev/toolkits/linkhut/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/linkhut/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/linkhut/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/linkhut/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Linkhut connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Linkhut - Build a conversational loop where your agent can execute Linkhut 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 Linkhut MCP server, and what's possible with it? The Linkhut MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Linkhut account. It provides structured and secure access to all your saved bookmarks, so your agent can organize, tag, retrieve, and manage your URLs and references exactly how you want. - Bookmark organization and retrieval: Effortlessly ask your agent to fetch, list, or filter all bookmarks, including details like tags, notes, and timestamps. - Automated link saving: Let your agent add new bookmarks for important websites, articles, or resources—tagged and noted for easy discovery later. - Tag management and insights: Have your agent pull a list of all used tags, track how often tags appear, and help you keep your bookmark library organized. - Bookmark editing and updates: Direct your agent to modify titles, descriptions, or tags for any saved link so your collection always stays current and relevant. - Bookmark cleanup and removal: Ask your agent to delete obsolete or unwanted bookmarks, keeping your Linkhut workspace clean and focused. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `LINKHUT_ADD_BOOKMARK` | Add bookmark | Adds a new bookmark to LinkHut. The bookmark can be marked as private/public and read/unread, with optional tags and notes. | | `LINKHUT_DELETE_BOOKMARK` | Delete bookmark | This tool allows users to delete a bookmark from their Linkhut account by providing the bookmark URL. It operates independently and only requires the URL parameter to identify and remove the bookmark. | | `LINKHUT_GET_ALL_TAGS` | Get all tags | Retrieves a list of all tags and their usage counts for the authenticated user. No additional parameters required besides authentication. | | `LINKHUT_GET_BOOKMARKS` | Get bookmarks | Retrieves bookmarks from the user's Linkhut account with optional filtering. This tool fetches bookmarks from Linkhut and supports filtering by: - Tag: Filter by one or more tags (space-separated) - Date: Filter by a specific date (ISO8601 format) - URL: Get a specific bookmark by its exact URL - Meta: Request additional metadata about bookmarks Returns a list of bookmarks with details including URL, title/description, tags, extended notes, timestamp, privacy status (shared), and read status (toread). | | `LINKHUT_UPDATE_BOOKMARK` | Update Bookmark | This tool allows users to update an existing bookmark in LinkHut. The tool updates the metadata of a bookmark including its title, description, and tags. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Linkhut MCP server is an implementation of the Model Context Protocol that connects your AI agent to Linkhut. It provides structured and secure access so your agent can perform Linkhut 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 Linkhut 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 Linkhut 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 Linkhut 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 Linkhut **What's happening:** - You create a Linkhut only session through Composio - Composio returns an MCP HTTP URL that exposes Linkhut tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["linkhut"]) 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=["linkhut"], ) 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 Linkhut through Composio's Tool Router. The agent can perform Linkhut 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 Linkhut MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/linkhut/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/linkhut/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/linkhut/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/linkhut/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/linkhut/framework/codex) - [OpenClaw](https://composio.dev/toolkits/linkhut/framework/openclaw) - [Hermes](https://composio.dev/toolkits/linkhut/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/linkhut/framework/cli) - [Google ADK](https://composio.dev/toolkits/linkhut/framework/google-adk) - [LangChain](https://composio.dev/toolkits/linkhut/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/linkhut/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/linkhut/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/linkhut/framework/llama-index) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [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. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives. - [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions. - [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date. - [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page. - [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes. - [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights. - [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Linkhut MCP? With a standalone Linkhut MCP server, the agents and LLMs can only access a fixed set of Linkhut tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Linkhut 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 Linkhut tools. ### Can I manage the permissions and scopes for Linkhut while using Tool Router? Yes, absolutely. You can configure which Linkhut 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 Linkhut data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)