- LIVE
- FAILED
- SUCCESS
- HUMAN
Short answer
How can I track Flightcontrol deployments and jobs with my AI?
Your agent uses specific tools to pull the current state of your deployments and the execution history of your scheduler jobs. You get direct answers on whether a deployment finished or why a job failed. This breakdown shows how your agent handles those requests.
Status outcomes
Where your status updates land.
The AI processes your requests and routes the information to these outcomes.
LIVE
Real-time progress
The AI pulls current deployment states. You see exactly where your code sits in the pipeline.
FAILED
Error identification
The agent identifies failed job executions. You get the specific reason a scheduler task didn't finish.
SUCCESS
Deployment confirmation
Once a deployment hits its target state, your AI confirms it's live. You can then move to the next task.
HUMAN
Manual intervention
If a deployment hangs or a job fails repeatedly, the AI flags it for you. You decide when to step in and fix the infrastructure.
The workflow
What your AI does when status updates arrive.
The agent acts as a bridge between your Flightcontrol environment and your chat interface.
Check deployment state
The agent queries the current status of a specific deployment to see if it is still running or completed.
get_deployment_statusVerify job runs
The agent looks up the execution history of scheduler jobs to find recent successes or failures.
get_job_execution_statusAnalyze job failures
When a job fails, the agent retrieves the execution status to help you understand the outcome.
get_job_execution_statusReport progress
The agent summarizes the deployment or job state into a plain text update for you.
get_deployment_statusget_job_execution_status
Try it
Copy these to start.
Use these prompts to get immediate answers from your agent.
Starting points
Replace the bracketed text with your actual deployment IDs or job names.
Flightcontrol Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_V0rwIPVlSzVozAT6HiedbE6p9bh46ahn9xB9Lbbb/mcpClaude Desktop
Follow the steps below to connect in seconds.
- 1In Claude Desktop, open Settings → Connectors.
- 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
- 3Click Add and start a new chat — Flightcontrol capabilities are ready to use.
{
"mcpServers": {
"flightcontrol-aws-paas-deployments-mcp": {
"url": "https://edge.vinkius.com/vk_preview_V0rwIPVlSzVozAT6HiedbE6p9bh46ahn9xB9Lbbb/mcp"
}
}
}What is the current status of deployment [ID]?
Did the last scheduled job [Name] finish successfully?
Check the status of my most recent deployment.
Why did the job execution [ID] fail?
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Start here
Connect Flightcontrol once, then ask.
Just follow the link to authorize the connection. Your credentials stay encrypted and the tools become immediately available in your AI client.
Connect Flightcontrol to your AIFAQ
How this task behaves.
- 01
Can the AI trigger a new deployment?
No. This MCP only provides tools to read the status of existing deployments and jobs.
- 02
Can my AI fix a failed job automatically?
No. The AI can report why a job failed, but you must manually intervene to fix the issue or restart the job.
- 03
How real-time are these status updates?
The updates are as real-time as the Flightcontrol API. The AI fetches the latest data every time you ask.
- 04
Can I see the logs for a specific job?
The AI can check the execution status, which includes the outcome of the job.
- 05
Does the AI need my AWS keys?
No. You connect through Vinkius, which manages the connection securely so you don't have to paste keys into your AI chat.
More questions about Flightcontrol? The Connector page answers them. See everything the Flightcontrol Connector can do
Connect Flightcontrol to Claude, Cursor, ChatGPT & more
