# How to integrate Textrazor MCP with Pydantic AI ```json { "title": "How to integrate Textrazor MCP with Pydantic AI", "toolkit": "Textrazor", "toolkit_slug": "textrazor", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/textrazor/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/textrazor/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:28:18.993Z" } ``` ## Introduction This guide walks you through connecting Textrazor to Pydantic AI using the Composio tool router. By the end, you'll have a working Textrazor agent that can extract named entities from this news article, summarize key phrases in customer reviews, classify support tickets by topic automatically through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Textrazor account through Composio's Textrazor MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Textrazor with - [OpenAI Agents SDK](https://composio.dev/toolkits/textrazor/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/textrazor/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/textrazor/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/textrazor/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/textrazor/framework/codex) - [OpenClaw](https://composio.dev/toolkits/textrazor/framework/openclaw) - [Hermes](https://composio.dev/toolkits/textrazor/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/textrazor/framework/cli) - [Google ADK](https://composio.dev/toolkits/textrazor/framework/google-adk) - [LangChain](https://composio.dev/toolkits/textrazor/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/textrazor/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/textrazor/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/textrazor/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/textrazor/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 Textrazor - 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 Textrazor 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 Textrazor MCP server, and what's possible with it? The Textrazor MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Textrazor account. It provides structured and secure access to advanced natural language processing features, so your agent can extract entities, classify content, analyze grammar, and understand relationships within text—all automatically and at scale. - Entity and relationship extraction: Enable your agent to identify and classify people, places, organizations, and relationships from any text, powering intelligent content analysis and knowledge graph building. - Text classification and categorization: Automatically categorize documents, articles, or snippets using built-in or custom classifiers, making it easy to sort and organize large volumes of text data. - Grammatical and dependency analysis: Let your agent parse sentence structure, analyze grammatical relationships, and build dependency trees to support advanced linguistic understanding and text analytics. - Custom dictionary and classifier management: Allow the agent to create and update custom entity dictionaries and classifiers, tailoring analysis to specialized domains or business needs. - Phrase extraction and sentiment detection: Extract key phrases, multi-word expressions, and even detect logical entailments or word senses, enabling deeper insights from any written content. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `TEXTRAZOR_ACCOUNT_INFO` | Get Account Information | This tool retrieves comprehensive information about a TextRazor account, providing essential details about the account's status, usage, and limits. It returns an Account object containing properties such as the current subscription plan, concurrent request limits, and daily usage among others, making it crucial for monitoring API usage, managing requests, and ensuring compliance with subscription limits. | | `TEXTRAZOR_CLASSIFY_TEXT` | Classify Text | This tool will classify text into predefined categories using TextRazor's classification capabilities. It takes input text, optional cleanup mode and language, and returns a list of relevant categories with their confidence scores from the analysis. The tool supports various built-in classifiers including: - textrazor_iab: IAB QAG segments - textrazor_iab_content_taxonomy_3.0: IAB Content Taxonomy v3.0 (2022) - textrazor_mediatopics_2023Q1: Latest IPTC Media Topics (March 2023) - And other versions of these taxonomies | | `TEXTRAZOR_CUSTOM_CLASSIFIER_MANAGER` | Manage Custom Classifiers | This tool manages custom classifiers in TextRazor, allowing users to create, update, and manage custom classification categories. | | `TEXTRAZOR_DICTIONARY_MANAGER` | Dictionary Manager | Manage custom entity dictionaries in TextRazor for enhanced named entity recognition. This tool enables you to create and manage dictionaries of domain-specific entities (e.g., product names, company names, technical terms) that TextRazor will recognize in text analysis. Operations include: - Creating new dictionaries with configurable matching rules - Listing all available dictionaries - Retrieving dictionary details and configuration - Deleting dictionaries - Adding, retrieving, and removing dictionary entries Note: Dictionaries created here can be used in text analysis by specifying their IDs in the 'entityDictionaries' parameter of TextRazor analysis requests. | | `TEXTRAZOR_EXTRACT_ENTITIES` | Extract Named Entities from Text | Extract named entities (people, places, companies, etc.) from text using TextRazor's entity extraction API. The tool will identify and classify named entities within the provided text, returning detailed information about each entity including its type, confidence score, and relevance score. The API returns many entities by default; filter by `relevanceScore` and `confidenceScore` thresholds to retain only meaningful results. | | `TEXTRAZOR_TEXT_RAZOR_ANALYZE_CONTENT` | Analyze Content with TextRazor | A comprehensive content analysis tool that combines multiple TextRazor extractors to perform a complete analysis of the input text. This action allows users to analyze text content with multiple extractors in a single API call. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Textrazor MCP server is an implementation of the Model Context Protocol that connects your AI agent to Textrazor. It provides structured and secure access so your agent can perform Textrazor 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 Textrazor - 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 Textrazor - MCPServerStreamableHTTP connects to the Textrazor 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 Textrazor 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 Textrazor composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["textrazor"], ) 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 Textrazor endpoint - The agent uses GPT-5 to interpret user commands and perform Textrazor operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent textrazor_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[textrazor_mcp], instructions=( "You are a Textrazor assistant. Use Textrazor 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 - Textrazor 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 Textrazor.\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 Textrazor composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["textrazor"], ) 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 textrazor_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[textrazor_mcp], instructions=( "You are a Textrazor assistant. Use Textrazor 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 Textrazor.\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 Textrazor through Composio's Tool Router. With this setup, your agent can perform real Textrazor 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 + Textrazor 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 Textrazor MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/textrazor/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/textrazor/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/textrazor/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/textrazor/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/textrazor/framework/codex) - [OpenClaw](https://composio.dev/toolkits/textrazor/framework/openclaw) - [Hermes](https://composio.dev/toolkits/textrazor/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/textrazor/framework/cli) - [Google ADK](https://composio.dev/toolkits/textrazor/framework/google-adk) - [LangChain](https://composio.dev/toolkits/textrazor/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/textrazor/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/textrazor/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/textrazor/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/textrazor/framework/crew-ai) ## Related Toolkits - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. - [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team. - [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs. - [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code. - [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets. - [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Textrazor MCP? With a standalone Textrazor MCP server, the agents and LLMs can only access a fixed set of Textrazor tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Textrazor 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 Textrazor tools. ### Can I manage the permissions and scopes for Textrazor while using Tool Router? Yes, absolutely. You can configure which Textrazor 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 Textrazor data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)