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 CrewAI 10 tools — connect in under 2 minutes

Name: AirOps
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to AirOps through Vinkius, pass the Edge URL in the `mcps` parameter and every AirOps tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="AirOps Specialist",
    goal="Help users interact with AirOps effectively",
    backstory=(
        "You are an expert at leveraging AirOps tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in AirOps "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* 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.

When paired with CrewAI, AirOps becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call AirOps tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the AirOps MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

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

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from AirOps

Why Use CrewAI with the AirOps MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with AirOps through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

AirOps + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries AirOps for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries AirOps, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain AirOps tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries AirOps against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

AirOps MCP Tools for CrewAI (10)

These 10 tools become available when you connect AirOps to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

AirOps + CrewAI FAQ

Common questions about integrating AirOps MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.