How to integrate Mistral ai MCP with Pydantic AI

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Introduction

This guide walks you through connecting Mistral ai to Pydantic AI using the Composio tool router. By the end, you'll have a working Mistral ai agent that can summarize this research paper in simple terms, generate python code for sorting a list, explain the difference between ai and ml through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Mistral ai account through Composio's Mistral ai MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Mistral ai with

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 Mistral ai
  • 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 Mistral ai 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 Mistral ai MCP server, and what's possible with it?

The Mistral ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mistral ai account. It provides structured and secure access to your Mistral AI models, so your agent can perform actions like generating text, summarizing content, answering questions, extracting structured information, and handling advanced language tasks on your behalf.

  • Text generation and completion: Have your agent produce coherent, context-aware text responses, complete prompts, or generate creative content leveraging Mistral's advanced models.
  • Summarization and paraphrasing: Ask your agent to summarize lengthy documents or rephrase input text for improved clarity or brevity.
  • Question answering and information extraction: Let your agent answer questions, extract key facts, or pull structured data from unstructured content automatically.
  • Content classification and sentiment analysis: Enable your agent to categorize text, detect topics, or analyze sentiment to inform downstream workflows.
  • Conversational AI and dialogue management: Build rich, multi-turn conversations or chatbots that handle context, intent, and user queries seamlessly using Mistral's models.

Supported Tools & Triggers

Tools
Append to conversationTool to append new entries to an existing conversation in Mistral AI.
Create AgentTool to create a new AI agent with custom configuration (Beta).
Create Agents CompletionTool to generate completions using a Mistral AI agent with specific instructions and tools.
Create Audio TranscriptionTranscribe audio files to text using Mistral AI's Voxtral models.
Create Chat CompletionGenerate conversational responses from Mistral AI models.
Create Chat ModerationTool to classify chat content for moderation purposes across 9 categories.
Create EmbeddingsTool to generate vector embeddings for input text using Mistral AI embedding models.
Create FIM CompletionGenerate code completions using fill-in-the-middle functionality.
Create libraryTool to create a new document library.
Create library shareCreate or update sharing permissions for a library.
Create ModerationTool to classify text content for moderation purposes across 9 categories.
Create OCRExtract text and structured data from images and documents using Mistral AI's OCR capabilities.
Create or Update Agent AliasTool to create or update an agent version alias.
Delete agentPermanently deletes an agent by its ID (Beta feature).
Delete ConversationTool to delete a conversation by its ID (Beta).
Delete FileDelete a file by its ID from Mistral AI.
Delete libraryPermanently deletes a library and all of its documents from Mistral AI.
Delete library documentPermanently deletes a document from a Mistral AI library.
Delete library shareRemove sharing permissions for a library from a user, workspace, or organization.
Download FileDownload the content of a previously uploaded file from Mistral AI.
Get AgentTool to retrieve details of a specific Mistral AI agent by its ID.
Get Agent VersionRetrieve a specific version of an agent (Beta).
Get ConversationTool to retrieve details of a specific conversation.
Get Conversation HistoryRetrieve the full history of a conversation in Mistral AI.
Get Conversation MessagesRetrieve all messages from a Mistral AI conversation.
Get document extracted text URLRetrieve a signed URL to download the extracted text from a document in a Mistral AI library.
Get document signed URLGet a signed URL to download a document from a Mistral AI library.
Get Document StatusRetrieve the processing status of a document in a Mistral AI library.
Get Document Text ContentRetrieve the extracted text content of a specific document from a Mistral AI library (Beta).
Get File Signed URLGet a time-limited signed URL for downloading a file from Mistral AI.
List Fine Tuning JobsList fine-tuning jobs with optional filtering and pagination.
Get libraryRetrieve detailed information about a specific library.
Get Library DocumentRetrieve metadata for a specific document in a Mistral AI library.
Get ModelTool to retrieve detailed information about a specific Mistral AI model by its ID.
List agent aliasesRetrieve all aliases for an agent version.
List AgentsTool to list all configured agents (Beta).
List Agent VersionsList all versions of a specific agent.
List Batch JobsTool to retrieve a list of all batch jobs with optional filtering and pagination.
List ConversationsList all created conversations (Beta).
List FilesTool to list all files available to the user.
List librariesList all document libraries accessible to your organization.
List Library DocumentsList all documents in a Mistral AI document library.
List library sharesList all sharing permissions for a document library.
List ModelsTool to retrieve all available Mistral AI models including base models and fine-tuned models.
Reprocess documentReprocess a document in a Mistral AI library (Beta).
Restart ConversationTool to restart a conversation from a specific point (Beta).
Retrieve FileRetrieve metadata of a file uploaded to Mistral AI.
Start ConversationTool to start a new conversation with a Mistral AI agent or base model.
Update AgentTool to update an existing agent's configuration.
Update agent versionTool to update the current version of an agent (Beta).
Update libraryTool to update an existing document library's properties.
Update library documentUpdate the metadata of a document in a Mistral AI library.
Upload FileUpload a file to Mistral AI for use in fine-tuning, batch processing, or OCR.
Upload Library DocumentUpload a document to a Mistral AI library for use with RAG-enabled agents.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

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

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Mistral ai
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

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

Import dependencies

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()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Mistral ai
  • MCPServerStreamableHTTP connects to the Mistral ai MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

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 Mistral ai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["mistral_ai"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Mistral ai 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

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
mistral_ai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[mistral_ai_mcp],
    instructions=(
        "You are a Mistral ai assistant. Use Mistral ai tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Mistral ai endpoint
  • The agent uses GPT-5 to interpret user commands and perform Mistral ai operations
  • The instructions field defines the agent's role and behavior

Build the chat interface

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 Mistral ai.\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()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Mistral ai API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Mistral ai and Pydantic AI:

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 Mistral ai
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["mistral_ai"],
    )
    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
    mistral_ai_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[mistral_ai_mcp],
        instructions=(
            "You are a Mistral ai assistant. Use Mistral ai 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 Mistral ai.\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 Mistral ai through Composio's Tool Router. With this setup, your agent can perform real Mistral ai 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 + Mistral ai for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

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FAQ

What are the differences in Tool Router MCP and Mistral ai MCP?

With a standalone Mistral ai MCP server, the agents and LLMs can only access a fixed set of Mistral ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mistral ai 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 Mistral ai tools.

Can I manage the permissions and scopes for Mistral ai while using Tool Router?

Yes, absolutely. You can configure which Mistral ai 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 Mistral ai data and credentials are handled as safely as possible.

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