AirOps MCP. Orchestrate complex workflows and memory operations.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
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AirOps. This server connects your AI agent to professional workflow orchestration, agent management, and memory stores. You can run multi-step workflows synchronously or asynchronously, chat with specialized agents, and manage knowledge by adding documents or searching existing memory.
It handles all your AI operations through natural conversation, letting your agent do the heavy lifting.
What your AI agents can do
Add memory document
Adds a document to the memory store to expand the AI's knowledge base.
Cancel execution
Stops a currently running workflow or task.
Chat with agent
Starts a conversation with a specialized AI agent.
Your agent executes defined AirOps workflows, passing custom inputs and receiving structured output data.
Your agent engages in a conversation with a specialized AirOps agent to perform niche tasks or follow unique instructions.
Your agent adds documents to the memory store using add_memory_document and retrieves information by querying the store with search_memory_store.
Your agent handles file uploads and management using upload_file, making the files available for subsequent workflow steps.
Your agent checks the status of running workflows using get_execution_status or cancels long-running tasks with cancel_execution.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
019d754badd memory document
Adds a document to the memory store to expand the AI's knowledge base.
019d754bcancel execution
Stops a currently running workflow or task.
019d754bchat with agent
Starts a conversation with a specialized AI agent.
019d754bexecute workflow async
Starts a complex workflow that runs in the background and reports status later.
019d754bexecute workflow sync
Runs a workflow immediately and waits for the result.
019d754bget app details
Retrieves metadata and details about a specific AI application.
019d754bget execution status
Checks the current progress and status of a running workflow.
019d754blist apps
Lists all available AI applications deployed in the AirOps workspace.
019d754bsearch memory store
Queries the memory store (vector database) using a search query.
019d754bupload file
Uploads a file so the AI can use it as input for workflows or data extraction.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with AirOps, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
This server connects your AI agent directly to AirOps, giving it professional-grade workflow orchestration and agent management. Your agent handles complex AI operations through natural conversation, letting it execute multi-step workflows and manage knowledge without you having to write code. You can kick off tasks and manage data flow entirely through chat.
Running Workflows and Agents
Your agent can run defined AirOps workflows, passing custom inputs and getting structured output data. You can kick off a complex workflow that runs in the background using execute_workflow_async and check its progress later with get_execution_status. If you need immediate results, your agent runs the workflow right away with execute_workflow_sync.
You can also stop any running task at any time by calling cancel_execution. For specialized tasks, your agent chats with a specialized AI agent using chat_with_agent. To see what AirOps apps are available, your agent runs list_apps, and to get details on a specific app, it uses get_app_details.
Managing Knowledge and Files
Your agent manages your knowledge base by adding documents to the memory store using add_memory_document. It can also retrieve information by querying the memory store with search_memory_store. If the workflow needs external data, your agent uploads a file using upload_file, making that file available for subsequent steps. You can also use get_app_details to retrieve metadata about any specific AI application.
Putting it Together
Your agent uses these tools to build comprehensive operations. It uploads a file using upload_file, passes that file and custom parameters into a workflow run via execute_workflow_sync, and then uses search_memory_store to check the resulting data against your existing knowledge base. You can also start a conversation with a specialized agent using chat_with_agent to handle a niche task, all while keeping track of the whole process using get_execution_status.
How AirOps MCP Works
- 1 Subscribe to the AirOps server and input your AirOps API Key into your AI client.
- 2 Ask your agent to perform a task, like 'Run the Data Extractor workflow for this text' or 'Search my Knowledge Base for API guides'.
- 3 The agent executes the necessary tool calls (e.g.,
execute_workflow_sync,search_memory_store), processes the result, and reports the final structured data back to you.
The bottom line is, your agent handles the entire sequence of operations—from reading files to executing complex logic—using only natural conversation.
Who Is AirOps MCP For?
AI Engineers who need to automate complex LLM chains; Product Managers who must audit specialized agent outputs; Operations Leads integrating data extraction into existing processes; Data Specialists who manage and query large internal knowledge bases.
Automates the execution of complex LLM chains and monitors performance without writing boilerplate integration code.
Audits AI app outputs and manages specialized agent configurations on the fly, without needing a developer.
Integrates AI-driven data extraction and analysis into existing business processes by calling workflows.
Manages and searches internal knowledge bases by adding documents or querying the memory store.
What Changes When You Connect
- Manage complex task flows using
execute_workflow_sync. You pass specific parameters to an app and get structured data back instantly, avoiding multi-step manual execution. - Build deep domain knowledge by using
add_memory_document. Instead of relying only on prompt context, you enrich the AI's long-term memory with specific company documents. - Keep track of long jobs with
get_execution_status. If a workflow times out or gets stuck, you check its status and can cancel it withcancel_execution. - Handle diverse data sources by using
upload_file. You upload a CSV or PDF, and the workflow can process it as an input, which is impossible with simple chat commands. - Query proprietary information using
search_memory_store. You don't just ask a question; you query a structured vector database of your company's knowledge. - Interact with specialized AI agents via
chat_with_agent. This allows you to perform niche, pre-defined tasks that are too complex for a standard chat prompt.
Real-World Use Cases
Extracting structured data from a legal document
A paralegal receives a contract and needs names, dates, and clauses. They ask their agent to 'Process this document.' The agent first uses upload_file, then calls the 'Legal Data Extractor' workflow via execute_workflow_sync, which returns the structured JSON data they need.
Researching internal API authentication rules
A data specialist needs to know the current standard for connecting to the billing API. They ask the agent to 'Find the latest API docs.' The agent runs search_memory_store on the 'Knowledge Base,' returning specific snippets on REST API authentication and Webhook setup.
Running a large, multi-stage marketing campaign report
The marketing ops lead needs a report that aggregates data from three different systems. They ask the agent to 'Run the Campaign Report.' The agent uses execute_workflow_async and then monitors its progress using get_execution_status until the final report is ready.
Onboarding a new technical team member
The manager wants to give the AI agent the company's latest internal SOPs. They use add_memory_document to ingest the entire binder, making the knowledge available for all future chat_with_agent interactions.
The Tradeoffs
Treating the AI like a search bar
Asking, 'What are the API guides?' and hoping the AI remembers them. This only relies on the prompt context, which has a limited window.
→
Instead, use search_memory_store and provide the specific query parameters. This guarantees the agent searches your dedicated vector database, accessing knowledge beyond the immediate conversation window.
Relying on chat for complex logic
Telling the agent, 'Extract names, then summarize the document, then send an email.' The agent will fail at the handoffs because it only has conversational memory.
→
Define and use a specific workflow, like 'Data Processor.' Call it using execute_workflow_sync. This forces the agent to execute the logic sequentially, ensuring the handoffs work every time.
Ignoring file inputs
Asking the agent to 'Summarize this PDF.' If the PDF isn't properly uploaded, the agent has no way to access the document's content, and the task fails.
→
Always start by using upload_file with the document. Then, pass the file ID or reference to the workflow execution tool (execute_workflow_sync) so the model knows exactly what to process.
When It Fits, When It Doesn't
Use this server if your process requires multiple, distinct steps: data ingestion, external API calls, or structured data output. If you just need general chat or quick summaries based on the current conversation, you don't need it. However, if you need to manage specialized agents (chat_with_agent), run background jobs (execute_workflow_async), or query proprietary knowledge bases (search_memory_store), this is the right tool. Don't try to manage workflows manually in the chat; use the dedicated execution tools.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AirOps. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Today's workflow management is a mess of clicks and copy-pasting.
Right now, if you need to run an AI process, you have to jump between your data source, your workflow platform, and your chat interface. You upload a file in one spot, copy the resulting data into another tool, and then manually tell the AI what to do with it. It's slow, and it's easy to lose steps.
With AirOps, your agent handles the whole sequence. You just tell it, 'Run the process.' The agent uses `upload_file`, executes the workflow with `execute_workflow_sync`, and handles the structured output. You get the final data, not a series of steps.
AirOps MCP Server: Control agent execution and knowledge retrieval.
Before this, accessing company knowledge meant manually searching internal wikis, hunting down the right document, and hoping the content was indexed correctly. If the information wasn't in a document, the AI was blind.
Now, you use `search_memory_store`. Your agent queries your dedicated knowledge base, bringing the most relevant, structured information directly into the conversation. It's about making the knowledge actionable, not just searchable.
Common Questions About AirOps MCP
How do I use `execute_workflow_sync`? +
You call execute_workflow_sync and provide the workflow's UUID and necessary parameters. The agent runs the workflow and waits for the final, structured result before responding to you.
Is `execute_workflow_async` better for large jobs? +
Yes. Use execute_workflow_async for workflows that take more than a minute. It starts the job and gives you a Job ID. You then use get_execution_status to monitor its progress until it's finished.
Can I use `chat_with_agent` if I have a specific goal? +
Yes, but it's better to define the goal in a workflow. chat_with_agent is for general interaction with a specialized agent, while workflows (execute_workflow_sync) are for guaranteed, repeatable, structured operations.
What's the difference between `search_memory_store` and `list_apps`? +
list_apps shows you what workflows are available. search_memory_store lets you query the actual data—your company's knowledge—to inform the agent's decision-making.
How do I upload files for my workflows using `upload_file`? +
You first call upload_file to get a reference ID. Then, you pass that ID as an input parameter when running a workflow using execute_workflow_sync or execute_workflow_async. The workflow can then read and process the file content.
Can I stop a long-running task using `cancel_execution`? +
Yes, you call cancel_execution and provide the specific execution ID. This immediately sends a stop signal to the running task, preventing further computation and saving resources.
What information do I need to start using `add_memory_document`? +
You simply provide the document content and optionally a title or source. The tool handles indexing the data into the memory store, making it searchable for your AI agent later on.
Does `get_execution_status` show me the output data? +
No, get_execution_status only reports the current state (e.g., 'running', 'completed', 'failed') and progress percentage. To get the final output data, you must wait for the status to be 'completed' and then check the execution results.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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