How do I get a Mistral AI API Key?

Log in to the [**Mistral Console**](https://console.mistral.ai), go to **API Keys** in your workspace settings, click **Create new key** and copy it immediately. You'll need to set up billing in the admin portal first.

What models are available?

Use the `list_models` tool to see all available Mistral models. Key models include mistral-large-latest (most capable), mistral-small-latest (efficient), codestral-latest (code specialist), and mistral-embed for embeddings. Each has different context windows, capabilities and pricing.

Can I send multi-turn conversations?

Yes! Pass a messages array with alternating 'user', 'assistant' and 'system' roles. Each message has a 'role' and 'content' field. Mistral will continue the conversation based on the full message history.

Can I moderate content for safety?

Yes! Use the `moderate` tool with text input. It returns safety scores for categories including sexual, hate, violence, self-harm, criminal and other harmful content. This is useful for filtering user-generated content before processing.

Mistral AI MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Mistral AI
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Mistral AI through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Mistral AI Assistant",
            instructions=(
                "You help users interact with Mistral AI. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Mistral AI"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Mistral AI MCP Server

Connect your Mistral AI account to any AI agent and leverage European-built AI models through natural conversation.

The OpenAI Agents SDK auto-discovers all 10 tools from Mistral AI through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Mistral AI, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Model Discovery — List all available Mistral models with their IDs, capabilities and context windows
Chat Completions — Send conversations to Mistral models (large, small, codestral, nemo) and receive responses with configurable parameters
Embeddings — Generate vector embeddings for semantic search, similarity comparison and vector storage
Content Moderation — Check text for harmful categories (violence, hate, sexual, self-harm) with safety scores
File Management — List and delete uploaded files used for batch processing and document AI
Batch Processing — Create, track and cancel batch jobs for cost-effective asynchronous processing

The Mistral AI MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Mistral AI to OpenAI Agents SDK via MCP

Follow these steps to integrate the Mistral AI MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Mistral AI

Why Use OpenAI Agents SDK with the Mistral AI MCP Server

OpenAI Agents SDK provides unique advantages when paired with Mistral AI through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Mistral AI + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Mistral AI MCP Server delivers measurable value.

Automated workflows: build agents that query Mistral AI, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Mistral AI, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Mistral AI tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Mistral AI to resolve tickets, look up records, and update statuses without human intervention

Mistral AI MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Mistral AI to OpenAI Agents SDK via MCP:

cancel_batch

Provide the batch ID. This is useful if you submitted a large batch by mistake and want to stop further processing. Cancel a running batch job

chat

Requires the model ID (e.g. "mistral-large-latest", "mistral-small-latest", "codestral-latest") and messages array in JSON format. Each message must have a "role" ("user", "assistant" or "system") and "content" (text). Optionally set max_tokens, temperature (0-1), top_p (0-1) and tools array for function calling. Returns the assistant's response. Send a chat message to a Mistral model

create_batch

Requires the input file ID (containing JSONL requests) and the endpoint (e.g. "/v1/chat/completions"). Returns the batch with its ID for tracking. Use list_batches and get_batch to monitor progress. Create a batch processing job

delete_file

Provide the file ID from list_files. WARNING: this action is irreversible. Delete an uploaded file from Mistral

embeddings

Requires the model ID and text input (string or array of strings). Returns embedding vectors for each input text. Useful for semantic search, similarity comparison and vector database storage. Generate embeddings using Mistral

get_batch

Provide the batch ID. Get details for a specific batch job

list_batches

Each batch shows its ID, status (queued, running, succeeded, failed, cancelled), input/output file IDs and request counts. List batch processing jobs

list_files

Files are used for fine-tuning, batch processing and document AI. Each file shows its ID, filename, purpose, size and upload date. List files uploaded to Mistral

list_models

Each model returns its ID (e.g. "mistral-large-latest", "mistral-small-latest", "codestral-latest"), display name, capabilities and context window. Use this to discover which models are available and their IDs for use with the chat tool. List all available Mistral AI models

moderate

). Requires the input text (string or array). Returns safety scores for each category. Useful for content filtering and safety checks before processing user input. Moderate text content with Mistral

Example Prompts for Mistral AI in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Mistral AI immediately.

"Send a message to Mistral Large asking 'What is the capital of France?'"

"List all available Mistral models."

"Moderate this text: 'I want to learn about AI safety and content filtering.'"

Troubleshooting Mistral AI MCP Server with OpenAI Agents SDK

Common issues when connecting Mistral AI to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Mistral AI + OpenAI Agents SDK FAQ

Common questions about integrating Mistral AI MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.