diagnose_run

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds two genuinely useful behavioral facts: statistics on sampled data are explicitly flagged, and returned training content is untrusted data rather than instructions. It does not cover return shape, cost of large metric lists, or latency on wide series.

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?

Four tight sentences, front-loaded with the core purpose, then limitations, then routing, then a safety note. No filler and no restatement of the tool name.

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?

There is no output schema, but the enumerated computed statistics effectively describe what comes back, and the sampling and untrusted-data caveats cover the main trust concerns. The remaining gap is parameter-level guidance for a 6-parameter tool, which the description largely omits.

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 description coverage is only 33%, and the description compensates minimally: 'selected metrics' gestures at the metrics array and 'sampled data' hints at limit, but uid, axis, goal and stream receive no explanation at all. With most parameters undocumented in both schema and description, an agent must guess at axis and stream semantics.

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 verb ('Compute evidence') and enumerates exactly what is computed: first/last, extrema, recent direction, variability and adjacent jumps. This distinguishes it from siblings like get_metric_series, get_history and plot_metric, which surface or visualize raw series rather than derived evidence.

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 states a clear boundary ('No automatic causal diagnosis') and points the agent to alternatives for deeper investigation ('Use get_run/logs/tables and raw downloads to investigate'). What's missing is an explicit trigger condition for choosing this over get_metric_series for the same metrics.

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