Getting-started Prompts

Once your MCP server is connected, you can dive right in.

Here are a few prompts to try. They work out of the box with agents like Cursor, Claude, or VS Code:

💡 Starting your request with “Use groundcover” is a helpful nudge - it pushes the agent toward MCP tools and context.

Basic Prompts to Try

MCP supports complex, multi-step flows, but starting simple is the best way to ramp up.

Pull Logs

Prompt:

Use groundcover to get 5 logs from the workload news-service from the past 15 minutes.

Expected behavior: The agent should call query_logs and show recent logs for that workload.

Inspect a K8s Resource

Prompt:

Use groundcover to show the current state of the chat-app deployment.

Expected behavior: The agent should call query_entities with kind:Deployment name:chat-app | limit 1 and return the deployment's live-state fields (replicas, status, labels, etc.) or a summary of them.

Find Slow Workloads

Prompt:

Use groundcover to show the top 5 workloads by P95 latency.

Expected behavior: The agent should call query_traces with a query like * | stats by (workload) quantile(0.95, duration_seconds) as p95 | sort by (p95 desc) | limit 5 and return the top 5 workloads.

Investigate Issues

When something breaks, your agent can help investigate and make sense of it.

Paste an Issue Link

Prompt:

I got an alert for this critical groundcover issue. Can you investigate it?
https://app.groundcover.com/monitors/issues?...

Expected behavior: The agent should use query_issues, pull issue details, and kick off a relevant investigation using logs, traces, and metadata.

Investigate Multiple Issues

Prompt:

I got multiple alerts in the staging-env namespace. Can you help me look into them using groundcover?

Expected behavior: The agent should call query_issues with a scoped query like * | filter namespace:staging-env | sort by (_time desc) | limit 20 and walk through the returned issues one by one.

Run an Async RCA

Prompt:

Use groundcover to investigate why checkout errors spiked in production around 10:30 UTC.

Expected behavior: The agent may start async_investigate_rca, then poll get_async_job until the RCA report is ready. It should verify important conclusions with direct query tools before presenting them as confirmed facts.

Analyze Logs

Prompt:

Use groundcover to analyze error logs for the payments namespace in the last 30 minutes.

Expected behavior: The agent may start async_analyze_logs, poll get_async_job, and return a Markdown analysis of recurring log patterns, examples, and follow-up queries.

Generate a Dashboard

Prompt:

Use groundcover to draft a dashboard for checkout service health: request rate, error rate, p95 latency, and pod restarts.

Expected behavior: The agent may start async_generate_dashboard, poll get_async_job, and return a dashboard JSON with name, optional description, and preset. The tool does not save the dashboard in groundcover.

Automate Coding & Debugging

groundcover’s MCP can also be your coding sidekick. Instead of digging through tests and logs manually, deploy your changes and let the agent take over.

Iterate Over Test Results

Prompt:

Use groundcover to debug this code. For each test, print relevant logs with test_id, and dive into any error logs.

Expected behavior: The agent should update the code with log statements, deploy it, and use query_logs to trace and debug.

Deploy & Verify

Prompt:

Please deploy this service and verify everything works using groundcover.

Expected behavior: The agent should assist with deployment, then check for issues, error logs, and traces via groundcover.