How do I find my AirOps API Key?

Log in to your AirOps account, navigate to **Workspace Settings**, and look for the **API Keys** section. You can generate and copy your secure bearer token there.

What is the difference between synchronous and asynchronous execution?

Synchronous execution (`execute_workflow_sync`) keeps the connection open until the AI finishes. Asynchronous (`execute_workflow_async`) returns an ID immediately, allowing you to check the status later, which is better for long-running tasks.

Can I search through my uploaded documents?

Yes! Use the `search_memory_store` tool. It performs a semantic search across your managed memory stores, returning the most relevant document chunks for your query.

AirOps MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: AirOps
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect AirOps through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to AirOps "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in AirOps?"
    )
    print(result.data)

asyncio.run(main())

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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 AirOps MCP Server

Connect your AirOps account to your AI agent to unlock professional AI workflow orchestration and agent management. From executing complex multi-step workflows synchronously or asynchronously to interacting with specialized chat agents and managing managed memory stores, your agent handles your AI operations through natural conversation.

Pydantic AI validates every AirOps tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Workflow Orchestration — Execute and monitor AirOps apps and workflows, passing custom parameters and retrieving structured results
Agent Interaction — Chat directly with your specialized AirOps agents to perform niche tasks or leverage unique agent instructions
Memory Management — Search within managed memory stores (vector databases) and add documents to enrich your AI's domain knowledge
File Orchestration — Upload and manage files to be used as inputs for your AI workflows and data extraction tasks
Real-time Status — Monitor execution statuses and cancel long-running AI tasks directly from your chat interface

The AirOps MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI 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 AirOps to Pydantic AI via MCP

Follow these steps to integrate the AirOps MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from AirOps with type-safe schemas

Why Use Pydantic AI with the AirOps MCP Server

Pydantic AI provides unique advantages when paired with AirOps through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your AirOps integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your AirOps connection logic from agent behavior for testable, maintainable code

AirOps + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the AirOps MCP Server delivers measurable value.

Type-safe data pipelines: query AirOps with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple AirOps tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query AirOps and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock AirOps responses and write comprehensive agent tests

AirOps MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect AirOps to Pydantic AI via MCP:

add_memory_document

Enrich AI knowledge

cancel_execution

Stop a running task

chat_with_agent

Interact with AI agent

execute_workflow_async

Run workflow asynchronously

execute_workflow_sync

Best for quick tasks. Run workflow synchronously

get_app_details

Get app metadata

get_execution_status

Check execution progress

list_apps

List AI applications

search_memory_store

Search vector database

upload_file

Upload file for AI

Example Prompts for AirOps in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with AirOps immediately.

"List all AI apps in my AirOps workspace."

"Execute the 'Data Extractor' app (UUID: abc-123) with input 'Extract names from this text: John Doe visited London'."

"Search my 'Knowledge Base' memory store for 'API integration guides'."

Troubleshooting AirOps MCP Server with Pydantic AI

Common issues when connecting AirOps to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

AirOps + Pydantic AI FAQ

Common questions about integrating AirOps MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your AirOps MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.