dq-score-trend

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It does not mention ordering, aggregation period, pagination, or any side effects. For a time-series tool, critical behaviors like default time range and data granularity are missing.

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 description is a single concise sentence that is front-loaded with the key concept (time-series of scores). It is efficient but lacks additional context that could be added without becoming verbose.

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?

Given the tool has 3 parameters and no output schema, the description is insufficient. It does not explain default behavior (e.g., how far back the time-series goes), what the response looks like, or how parameters like 'scope' and 'extractFields' affect the output. Users are left guessing essential details.

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 67% (2 of 3 parameters have descriptions). The description does not add meaning beyond schema: the tool description mentions the source table but not parameter usage. The missing 'days' parameter description is not compensated by the tool description.

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 clearly states the tool returns a time-series of specific DQ scores (completeness, freshness, validity, anomaly_free, and overall_score) from a named source table. It distinguishes from siblings like dq-score-snapshot (snapshot vs. time-series) and dq-failed-checks-by-dataset (different focus).

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?

No guidance on when to use this tool versus alternatives like dq-score-snapshot or dq-failed-checks-by-dataset. The description does not provide context for appropriate usage or scenarios where other tools might be preferred.

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