# How to integrate Mopinion MCP with Pydantic AI ```json { "title": "How to integrate Mopinion MCP with Pydantic AI", "toolkit": "Mopinion", "toolkit_slug": "mopinion", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/mopinion/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/mopinion/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:19:29.427Z" } ``` ## Introduction This guide walks you through connecting Mopinion to Pydantic AI using the Composio tool router. By the end, you'll have a working Mopinion agent that can get all recent website feedback reports, summarize negative feedback from mobile users, list most common survey responses this week through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Mopinion account through Composio's Mopinion MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Mopinion with - [OpenAI Agents SDK](https://composio.dev/toolkits/mopinion/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/mopinion/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/mopinion/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/mopinion/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/mopinion/framework/codex) - [OpenClaw](https://composio.dev/toolkits/mopinion/framework/openclaw) - [Hermes](https://composio.dev/toolkits/mopinion/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/mopinion/framework/cli) - [Google ADK](https://composio.dev/toolkits/mopinion/framework/google-adk) - [LangChain](https://composio.dev/toolkits/mopinion/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/mopinion/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/mopinion/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/mopinion/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/mopinion/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 Mopinion - 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 Mopinion 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 Mopinion MCP server, and what's possible with it? The Mopinion MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mopinion account. It provides structured and secure access to your feedback data and analytics, so your agent can perform actions like retrieving user feedback, analyzing survey results, generating reports, and monitoring trends on your behalf. - Centralized feedback retrieval: Instantly pull user feedback from all your websites and apps so your agent can surface insights across every touchpoint. - Survey results analysis: Let your agent analyze form and survey submissions, identifying common issues, sentiment, and emerging trends. - Custom report generation: Have the agent generate detailed feedback reports, aggregating user responses and highlighting actionable improvements. - Trend and KPI monitoring: Ask your agent to track changes in user sentiment or conversion-related metrics over time for continuous optimization. - Segmented feedback filtering: Enable your agent to filter and group feedback by device, location, or other custom attributes to uncover targeted opportunities. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `MOPINION_GET_ACCOUNT` | Get Account | Tool to retrieve the current authenticated account information. Use to verify authentication and get account details. | | `MOPINION_GET_DATASET_BY_ID` | Get Dataset by ID | Retrieve complete metadata for a specific Mopinion dataset (feedback form) by its ID. Returns dataset properties including name, associated report ID, data source type, and description. Use this action when you need detailed information about a specific dataset/form. To discover available dataset IDs, use the List Datasets action first. This is a read-only operation that does not modify any data. | | `MOPINION_GET_DATASET_FEEDBACK` | Get Dataset Feedback | Retrieve feedback items (survey responses) for a specific dataset/form with optional pagination and filtering. Use this action to fetch multiple feedback entries from a dataset. Each feedback item contains the survey responses including custom fields, scores, metadata, and timestamps. Supports pagination for large result sets and filtering by field values. For retrieving a single feedback item by ID, use GET_DATASET_FEEDBACK_BY_ID instead. | | `MOPINION_GET_DATASET_FEEDBACK_BY_ID` | Get Dataset Feedback By ID | Retrieves a single feedback item by its unique identifier from a specific dataset. Use this tool when you need detailed information about a specific feedback item. First obtain the dataset_id using List Datasets, then get feedback_id values using Get Dataset Feedback. **Important Notes:** - Returns a warning in metadata if the feedback item doesn't exist (with null field values) - Returns 404 error if the dataset_id itself is invalid or inaccessible - The response includes feedback fields, tags, scores, timestamps, and associated metadata | | `MOPINION_GET_DATASET_FIELDS` | Get Dataset Field Definitions | Tool to retrieve field definitions for a dataset. Use when you need the schema of a dataset's fields. | | `MOPINION_GET_DEPLOYMENT_BY_ID` | Get Deployment by ID | Retrieves detailed configuration for a specific feedback form deployment by its ID. Returns deployment rules, trigger conditions, scheduling settings, target URLs, and active status. Use this to understand when, where, and how a feedback form is displayed to users. | | `MOPINION_GET_DEPLOYMENTS` | Get Deployments | Tool to list all deployments for the authenticated Mopinion account. Use after setting up authentication. | | `MOPINION_GET_REPORT_BY_ID` | Get Report By ID | Retrieves detailed information about a specific Mopinion report by its ID. Returns comprehensive report details including name, description, language, creation date, and all associated datasets with their metadata. Use this when you need full information about a specific report. To find available report IDs, use the Get Reports action first. Example use case: Get complete details of report 22511 to understand its datasets and configuration. | | `MOPINION_GET_REPORT_FEEDBACK` | Get Report Feedback | Tool to retrieve feedback items for a report. Use when you need paginated and filtered feedback entries for analysis. | | `MOPINION_GET_REPORT_FIELDS` | Get Report Fields | Tool to retrieve field definitions for a specific report. Use when you need the schema of a report's feedback fields before constructing or analyzing forms. | | `MOPINION_GET_ROOT` | Get API Root | Check Mopinion API availability and get version information. Pings the API health check endpoint (/ping) and returns status code, 'pong' response, and current API version. Use this to verify the API is reachable and operational. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Mopinion MCP server is an implementation of the Model Context Protocol that connects your AI agent to Mopinion. It provides structured and secure access so your agent can perform Mopinion 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 Mopinion - 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 Mopinion - MCPServerStreamableHTTP connects to the Mopinion 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 Mopinion 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 Mopinion composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["mopinion"], ) 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 Mopinion endpoint - The agent uses GPT-5 to interpret user commands and perform Mopinion operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent mopinion_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[mopinion_mcp], instructions=( "You are a Mopinion assistant. Use Mopinion 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 - Mopinion 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 Mopinion.\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 Mopinion composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["mopinion"], ) 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 mopinion_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[mopinion_mcp], instructions=( "You are a Mopinion assistant. Use Mopinion 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 Mopinion.\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 Mopinion through Composio's Tool Router. With this setup, your agent can perform real Mopinion 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 + Mopinion 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 Mopinion MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/mopinion/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/mopinion/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/mopinion/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/mopinion/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/mopinion/framework/codex) - [OpenClaw](https://composio.dev/toolkits/mopinion/framework/openclaw) - [Hermes](https://composio.dev/toolkits/mopinion/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/mopinion/framework/cli) - [Google ADK](https://composio.dev/toolkits/mopinion/framework/google-adk) - [LangChain](https://composio.dev/toolkits/mopinion/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/mopinion/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/mopinion/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/mopinion/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/mopinion/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. 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It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Mopinion MCP? With a standalone Mopinion MCP server, the agents and LLMs can only access a fixed set of Mopinion tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mopinion 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 Mopinion tools. ### Can I manage the permissions and scopes for Mopinion while using Tool Router? Yes, absolutely. You can configure which Mopinion 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 Mopinion data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)