# How to integrate Linkhut MCP with Pydantic AI ```json { "title": "How to integrate Linkhut MCP with Pydantic AI", "toolkit": "Linkhut", "toolkit_slug": "linkhut", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/linkhut/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/linkhut/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:17:42.375Z" } ``` ## Introduction This guide walks you through connecting Linkhut to Pydantic AI 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 Pydantic AI 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) - [CrewAI](https://composio.dev/toolkits/linkhut/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Linkhut - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Linkhut workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## 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 with an active API key - Basic familiarity with Python and async programming ### 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 Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Linkhut - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Linkhut - MCPServerStreamableHTTP connects to the Linkhut MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Linkhut tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Linkhut composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["linkhut"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Linkhut endpoint - The agent uses GPT-5 to interpret user commands and perform Linkhut operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent linkhut_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[linkhut_mcp], instructions=( "You are a Linkhut assistant. Use Linkhut tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Linkhut API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Linkhut.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Linkhut composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["linkhut"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent linkhut_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[linkhut_mcp], instructions=( "You are a Linkhut assistant. Use Linkhut tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Linkhut.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Linkhut through Composio's Tool Router. With this setup, your agent can perform real Linkhut actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Linkhut for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## 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) - [CrewAI](https://composio.dev/toolkits/linkhut/framework/crew-ai) ## 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 Pydantic AI? Yes, you can. Pydantic AI 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)