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Latency is stored as four disjoint counters, not as a percentile. Disjoint, not cumulative: a 300ms call is counted in 100–500ms only.

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Why buckets

Percentiles cannot be summed. Storing a p95 per day and averaging it across a month gives a number that is not the p95 of anything. Four disjoint counters can be summed, and they aggregate correctly across any date range and any set of tools. A histogram of four buckets is enough to answer the question anyone actually has — is this tool slow? — without pretending to a precision the storage does not have.

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What p95 means here

The p95 latency card walks the cumulative counts to find the bucket holding the 95th-percentile call, and reports that bucket’s upper edge. So a p95 of 500ms means “the 95th-slowest call is somewhere in the 100–500ms band” — an upper bound, not the value itself. Interpolating inside the bucket would invent precision the four counters do not have. The top bucket is open-ended, so a p95 that lands there has no numeric edge and reads as >2s. With no calls at all, the card reads a dash rather than zero — an absent measurement, not a fast one.

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The distribution chart

The same four counters as a chart, with shares that sum to 1. Rendered on the overview server-wide and on each tool page scoped to that tool, from the same component and the same shape.

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Reading it

The bucket that matters most is >2s. Two seconds is roughly where an agent’s user starts noticing it is waiting, and the slow tool insight fires when more than 10% of a tool’s calls land there. A long tail of slow calls with a fast p50 is usually one of:
  • an unbounded query that gets slow with large inputs
  • a cold cache or a cold connection pool
  • an upstream API you do not control, on its own bad days
The distribution tells the three apart better than any single number: the first spreads across buckets as inputs grow, the second is a thin spike, the third is bursty over the calls per day chart.

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Latency and cost are the two where rising is bad

The delta on the p95 card colours a rise as bad, while the arrow follows the actual change. That verdict is decided API-side rather than in the interface, so the REST API and the MCP tools reach the same conclusion without re-deriving it.