Measure how much of the pager is duplicates
Groups a month of Datadog, Sentry and Better Stack alerts into the underlying issues, and puts a number on the noise.
I want to do this: Group last month's pages into distinct issues. How many were the same thing twice? ## Setup (skip if Polylane is already set up) Read and follow https://polylane.com/auth.md for non-interactive signup and setup. Start by checking whether I am already signed in; reuse my account and workspace. If the CLI is missing, bootstrap it without starting the interactive wizard: curl -fsSL 'https://polylane.com/setup?ref=prompts' | bash -s -- --install-only Then follow the guide through email verification, workspace selection, source connections, and MCP authentication. Ask me for an email code or OAuth consent only when needed. Verify each step; report pending setup instead of claiming success from installation alone. ## How to work Over MCP: searchTools lists what this workspace exposes, with each tool's schema; call it first. runTool runs one tool, runCode chains several in one call and returns just the answer. search and execute cover the full Polylane REST API: threads, issues, investigations, autofixes, memories. From the terminal: the polylane CLI wraps the same API, with structured output and non-interactive flags everywhere. Reads always work. Write tools appear only if I have opted in, and every write is screened. ## Task: Measure how much of the pager is duplicates Steps: 1. Pull every alert and page from the window across providers 2. Group them into underlying issues: same signal, same resource, same window 3. Count the duplicates and the storms, per provider and per service 4. Flag the alerts that were never once real 5. Deliver the report with the noisiest offenders ranked Ground every claim in data you actually pulled: the query, the log line, the change record. If the data is inconclusive, say so. Ask me before anything that writes.
Forty pages, six actual problems.
Alert storms, repeat alerts and providers re-grouping them inflate every incident into a pile. The rotation burns out on volume that was mostly echoes.
- The same regression paging four people through three providers
- No number on how noisy the pager actually is
- Alert fatigue treated as a personality trait
One prompt, this much work. Every step on your real data.
- 1 Pull every alert and page from the window across providers
- 2 Group them into underlying issues: same signal, same resource, same window
- 3 Count the duplicates and the storms, per provider and per service
- 4 Flag the alerts that were never once real
- 5 Deliver the report with the noisiest offenders ranked
A number on the noise
The pager's real information rate, measured against what turned out to matter. The case for fixing the noisiest alerts writes itself.
What broke overnight? “Triage everything that fired overnight: what was real, what was noise, and what's still open?” Root cause 
“P99 on checkout-api doubled at 14:10. Find the cause and show the evidence.” Postmortem “Write the postmortem for yesterday's incident: timeline, root cause, and the follow-ups.” First responder 
“We're seeing 500s on api.example.com. Start an investigation and post findings to #incidents.” What changed? 
“Did anything deploy or change config in the last two hours that could explain this latency?” War-room brief “Summarise the open incident for the exec channel: impact, cause so far, next steps.” Close the loop “The incident is resolved. Draft the permanent fix and link the evidence.” Which release? “Errors started around Tuesday. Which release introduced them?” Audit the alerts “Go through our alert rules: which ones fire the most, and which were ever real?”
“Triage everything that fired overnight: what was real, what was noise, and what's still open?” Root cause 
“P99 on checkout-api doubled at 14:10. Find the cause and show the evidence.” Postmortem “Write the postmortem for yesterday's incident: timeline, root cause, and the follow-ups.” First responder “We're seeing 500s on api.example.com. Start an investigation and post findings to #incidents.” What changed? “Did anything deploy or change config in the last two hours that could explain this latency?” War-room brief “Summarise the open incident for the exec channel: impact, cause so far, next steps.” Close the loop “The incident is resolved. Draft the permanent fix and link the evidence.” Which release? “Errors started around Tuesday. Which release introduced them?” Audit the alerts “Go through our alert rules: which ones fire the most, and which were ever real?”