get_data_quality

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

Annotations already declare readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds real interpretive context beyond that: it flags that API-limited metrics exist and that missing data must not be read as zero, which shapes how an agent interprets the result. It stops short of describing the returned report's structure.

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?

A single front-loaded sentence listing exactly what gets diagnosed, with no filler or repetition of the tool name. Every clause carries distinct information.

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?

With six optional parameters, an enum, an array of 39 data types, and no output schema, the description should sketch what the diagnostic returns (per-type coverage? per-source counts?). It defines the scope well but leaves the result shape entirely unspecified, so an agent cannot predict what it will get back.

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 50% – source, deduplicate, and source_priority are documented in-schema, while days, timezone, and data_types are not. The description gestures at recency ('recent') and provenance, but adds no detail on lookback window bounds, timezone handling, or filtering the 39 data types. Partial compensation, not full.

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?

Names a specific verb ('Diagnose') plus the exact resources examined: data coverage, missing records, source provenance, and API-limited metrics. The closing clause ('without treating missing data as zero') gives it a distinct identity as a quality-audit tool, separating it from value-returning siblings like get_metrics or get_recent_metrics.

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?

Use is implied by the diagnostic framing – you call it to check whether data is trustworthy before analyzing it – but the description never states when to prefer it over get_sync_status, get_data_source_info, or list_available_metrics. No exclusions or alternative routing are given.

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