get_latency_summary

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the disclosure burden; it openly lists the KPI set, the per-day timeseries, and the per-stage pipeline breakdown, including stage names. While it does not explicitly state read-only behavior or empty-data handling, the get-verb and output detail make the safety and return profile largely transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The main purpose is front-loaded, followed by a compact enumeration of returned metrics and stage names, closing with a one-sentence routing hint. Every phrase earns its place, and the format is easy to scan despite the detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Because there is no output schema, the description does the necessary work of explaining return values: percentiles, call counts, timeseries, and per-pipeline-stage averages. Combined with the schema's date/filter details, a caller has enough information to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%; all three parameters have descriptions in the schema, so the baseline 3 applies. The description reinforces the date-range scope but adds no new parameter semantics beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource (perceived-latency KPIs), a scoped operation (org-level over a date range), and the exact metrics and breakdowns returned. It also explicitly names debug_call as the alternative for single-call granularity, making it easy to distinguish from sibling analytics tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly states that debug_call should be used for one call's per-turn numbers, providing a when-not and an alternative. It does not contrast with get_duration_stats or get_usage_stats, but the org/date-range framing gives enough context to select this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.