# AirOps MCP for AI Agents AI Agent Connect

> AirOps is an orchestration layer for your AI agents. It lets you run multi-step workflows, interact with specialized agents, and query managed memory stores directly from your chat interface. It turns complex LLM operations into simple commands.

## Overview
- **Category:** ai-frontier
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_0guLp9ho3nBBv2oVWQwh3EyBDOaKJy251N5l5oxu/ai-agent-connect
- **Tags:** ai-workflows, agent-management, llm-ops, automation, model-deployment, memory-management

## Description

AirOps lets you run multi-step workflows and manage agents without the manual overhead of stitching calls together. You're probably tired of jumping between tabs to see if a workflow finished or trying to manually feed data into a specific agent. This Connector changes that by giving your agent a direct line to your account. Instead of manually stitching together LLM calls, you can just tell your agent to run a specific app or check the status of a long-running task. It handles the heavy lifting of orchestration while you stay in the chat interface. You can even pull from managed memory stores to give your agent the specific context it needs for a job. It's one of the best ways to manage your agentic infrastructure when using the Vinkius catalog because it brings everything into one place. You get to see results in real time and manage files for data extraction without the usual friction of moving data around between systems.

## Tools

### list_apps
See all the AI applications in your workspace. You'll know exactly what's available to run at any time.

### get_app_details
Pull the metadata for a specific app. This helps you understand the configuration and requirements for a task.

### execute_workflow_sync
Run a workflow and wait for the result immediately. This is perfect for quick tasks that need an instant answer.

### execute_workflow_async
Start a workflow and let it run in the background. Use this for longer tasks that don't need to finish right away.

### get_execution_status
Check how a background task is doing. You can see the progress of long-running workflows in real time.

### cancel_execution
Stop a task that's taking too long or isn't working right. This gives you control over your active workflows.

### chat_with_agent
Send a message to a specialized agent. This lets you tap into niche instructions for specific jobs.

### search_memory_store
Query your vector database for specific info. This pulls relevant context from your managed memory stores.

### add_memory_document
Add new files to your knowledge base. This keeps your AI's domain knowledge up to date.

### upload_file
Put files into AirOps for your AI to use. This makes it easy to feed large documents into your workflows.

## Prompt Examples

**Prompt:** 
```
List all AI apps in my AirOps workspace.
```

**Response:** 
```
I've retrieved your apps. You have 12 active AirOps applications, including:

*   **Content Summarizer**
*   **Data Extractor v2**
*   **Legal Assistant**

Would you like the technical details for any of them?
```

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

**Response:** 
```
I've executed the workflow. The AI has processed your request and returned the following structured data:

| Field | Value |
| :--- | :--- |
| **names** | John Doe |
| **locations** | London |

Would you like to save this result to a document?
```

**Prompt:** 
```
Search my 'Knowledge Base' memory store for 'API integration guides'.
```

**Response:** 
```
I've searched the 'Knowledge Base' memory store. I found 2 relevant document snippets discussing:

1.  **REST API Authentication**
2.  **Webhook Setup**

Would you like me to summarize these guides for you?
```

## Capabilities

### Run multi-step workflows
Execute complex sequences of actions and get structured data back.

### Talk to specialized agents
Chat with niche bots that have custom instructions for specific tasks.

### Search vector databases
Query your managed memory stores to find relevant context instantly.

### Upload data files
Provide documents as inputs for your AI workflows and data extraction.

### Monitor task progress
Check the status of long-running jobs and cancel them if needed.

## Use Cases

### Extracting data from messy PDFs
A user uploads a document and asks the agent to pull all dates and names. The agent uses upload_file and execute_workflow_async to return a clean table.

### Searching internal knowledge
A team member asks for a specific policy. The agent uses search_memory_store to scan the vector database and summarize the relevant sections.

### Delegating to a legal bot
A user needs a contract review. The agent uses chat_with_agent to pass the request to a specialized legal bot for a high-accuracy response.

### Checking batch job status
An engineer wants to know if a data migration is done. The agent uses get_execution_status to provide a live progress update.

## Benefits

- Run complex multi-step LLM chains without manual intervention by using execute_workflow_sync for immediate results.
- Manage your agentic fleet more easily with chat_with_agent to trigger niche tasks on specialized bots.
- Keep your AI's knowledge fresh by using add_memory_document to feed new data into your vector stores.
- Monitor long-running data extraction jobs in real time with get_execution_status so you're never left guessing.
- Handle large-scale data inputs effortlessly with upload_file to provide the raw materials for your workflows.

## How It Works

The bottom line is you get a direct control panel for your AI workflows inside any chat app.

1. Connect your AirOps API key to your Connector client.
2. Select the apps or workflows you want to run via natural language.
3. Get results or status updates directly in your chat interface.

## Frequently Asked Questions

**Can I use the AirOps MCP to run multi-step workflows?**
Yes. You can trigger complex sequences of actions and get structured data back without having to manually click through different steps.

**How does the AirOps MCP handle long-running tasks?**
It allows your agent to start a task in the background and then check back later for a status update, so your chat doesn't get hung.

**Can I use AirOps MCP to search my own documents?**
Yes, if you connect your memory stores, your agent can search through your specific documents and knowledge base to find relevant info.

**Does the AirOps MCP support file uploads?**
It does. You can upload files directly for your agent to use as inputs for data extraction or other automated workflows.

**Can I talk to specialized agents using AirOps MCP?**
Yes, you can interact with specific bots that have their own unique instructions for niche tasks like legal review or data cleanup.

**Is AirOps MCP good for data extraction?**
It's a great fit. It helps you automate pulling specific data points out of documents and putting them into a structured format.

**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.