# AirOps MCP for AI Agents MCP > AirOps handles professional AI workflow orchestration and agent management. It lets your AI client execute complex, multi-step pipelines, interact with specialized agents, and query private knowledge bases—all through simple conversation. Build sophisticated LLM applications without writing boilerplate code. ## Overview - **Category:** ai-frontier - **Price:** Free - **Tags:** ai-workflows, agent-management, llm-ops, automation, model-deployment, memory-management ## Description Running advanced AI models used to mean managing dozens of separate APIs, checking status endpoints, and manually feeding data between services. Now, you talk to your agent, and it handles the whole process. This MCP lets you treat complex automation like a natural conversation. You can instruct your agent to execute multi-step workflows, passing custom parameters as if you were talking to a human coworker. Need to reference internal policies? The agent searches managed memory stores—your private knowledge base—and uses that context to answer. If the job is huge and takes minutes, you just ask it to run in the background, and your client tracks the status until it's done. Whether you need to upload a file for data extraction or simply chat with a niche expert agent, this MCP manages all the connections through your preferred AI client. It’s designed to make building reliable, production-grade LLM applications feel less like engineering and more like talking. ## Tools ### upload_file Allows you to upload source files that serve as inputs for AI processing tasks. ### list_apps Retrieves a list of all available AI applications configured in the workspace. ### get_app_details Fetches specific metadata and details about a particular AI application. ### execute_workflow_sync Runs an entire multi-step workflow immediately, best for quick tasks that need instant results. ### execute_workflow_async Starts a long-running workflow in the background so you can continue working while it processes. ### get_execution_status Checks if an initiated task is complete, failed, or still running. ### cancel_execution Stops a workflow that has started but needs to be halted before completion. ### chat_with_agent Enables conversational interaction with a specialized AI agent for specific tasks. ### search_memory_store Searches the knowledge base, finding relevant document snippets based on your query. ### add_memory_document Adds new documents or information to the memory store to expand the AI's domain knowledge. ## Prompt Examples **Prompt:** ``` I need to understand our new vendor agreement terms quickly. ``` **Response:** ``` **Vendor Agreement Summary** * **Effective Date:** 2024-11-01 * **Key Clause:** Indemnity is mutual and capped at $5M. * **Action Item:** Must be approved by legal before signing. I found the relevant sections in the knowledge base. Would you like me to draft a summary email for review? ``` **Prompt:** ``` Run the 'Client Data Extractor' app on this file and give me the JSON. ``` **Response:** ``` **Workflow Execution Complete** ✅ **Status:** Success (Sync) ```json { "client_id": "CUST-9012", "name": "Jane Doe", "service_tier": "Premium" } ``` Do you want to save this result? ``` **Prompt:** ``` Check the status of the large report I started an hour ago. ``` **Response:** ``` **Job Status Check** * **Task:** Q4 Market Analysis Report (Async) * **Status:** Processing * **Progress:** 65% Complete * **ETA:** Approximately 12 minutes remaining. You can use the chat interface to cancel this job if you need to switch focus. ``` ## Capabilities ### Run Structured Workflows You execute multi-step data pipelines quickly or run them in the background for long tasks. ### Interact with Specialized Agents You chat directly with niche AI agents built for specific business functions, like legal analysis or content summarizing. ### Query Internal Knowledge Bases The agent searches and retrieves information from your private document repository to inform its answers. ### Manage Files for Inputs You upload source files, allowing the AI to use them directly for data extraction or analysis. ### Track Long-Running Tasks The system monitors execution progress and lets you cancel tasks that stall or take too long. ## Use Cases ### Generating Compliance Reports from Internal Docs A data specialist asks their agent for a report on 'Q3 privacy violations.' The agent first searches memory store using search_memory_store, pulls relevant policy documents, and then executes an application to summarize the findings into a structured PDF. ### Processing Batch Customer Feedback A product manager uploads hundreds of customer transcripts via upload_file. The agent runs a workflow synchronously using execute_workflow_sync, extracting sentiment and key feature requests from every file in one go. ### Debugging a Broken Marketing Chain An AI engineer notices a scheduled task failing. Instead of guessing, they use get_execution_status to check the failure point, then cancel_execution if it's stuck looping on an error. ### Creating a Niche Content Summarizer A marketer needs quick summaries for specific topics. They chat with agent using chat_with_agent and guide the conversation to produce several structured content outlines in minutes. ## Benefits - The AirOps MCP lets you execute multi-step processes like an expert. You don't need to write separate code blocks; your agent handles the flow. - Need context? Instead of manually searching databases, simply ask the agent to search memory store and it uses the retrieved data immediately. - Running a job that takes 20 minutes? Use execute_workflow_async. You start the task now and come back later to check its status with get_execution_status. - You can manage multiple applications without switching tabs. Start by listing all apps, then dive into details for any specific tool you need. - If a job goes wrong or takes too long, you don't get stuck. You use cancel_execution to stop the process and debug what went wrong. ## How It Works The bottom line is that you speak naturally to your agent, and it manages all the complex backend orchestration automatically. 1. Subscribe to the AirOps MCP and provide your API key. 2. Use your AI client to initiate a task, such as asking it to run an application or search memory. 3. Your agent executes the operation in the background, providing real-time status updates until the result is ready for you. ## Frequently Asked Questions **How does AirOps help me build complex AI workflows without coding?** AirOps lets you orchestrate multi-step processes using natural conversation. You don't write API calls; you simply tell your agent to perform a sequence of actions, like 'fetch data' then 'summarize it,' and the MCP manages the flow. **Can AirOps connect my private company documents so my AI can answer questions?** Yes. You use the memory management tools to upload your internal documents. The agent searches this dedicated knowledge base, ensuring the answers it provides are based on *your* specific policies and data. **What if I need to run a job that takes hours? Does AirOps handle that?** The MCP supports asynchronous workflow execution. You start the long task, and your agent monitors it in the background until it's done, giving you status updates without freezing your chat session. **Is AirOps only for data extraction? Can I use it for general tasks?** Not at all. While it excels at structured data and workflows, you can also use the agent to interact with specialized conversational agents (chat_with_agent) for niche Q&A or content generation. **How do I make sure my AI uses up-to-date information?** You enrich your AI's context by managing memory stores. You can add new documents or update policies, and the agent will use that most current knowledge when responding to queries.