Identify Dataset Update Anomalies

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

Annotations already cover safety (readOnly, openWorld, idempotent, non-destructive), so the description's added behavioral details are useful: results come from the 'latest published anomaly detection', are ranked by interval/threshold deviation, and include pipeline-computed evidence rather than requiring the agent to calculate it. This adds meaningful behavioral context beyond the annotations, though it does not discuss rate limits or pagination.

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?

Two concise sentences: the first front-loads the core purpose and ranking behavior, the second adds the optional filter and a key workflow benefit. Every sentence earns its place with no fluff.

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 that the tool is a read-only query with three optional parameters, a full output schema, and complete parameter descriptions, the description is sufficient: it explains what is returned, the ordering, the optional reliability filter, and that evidence is precomputed. No critical operational detail is missing.

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 100%, so the schema fully documents mode, limit, and min_reliability. The description adds no new semantic detail for mode or limit and only restates the reliability option in nearly the same words as the schema. Baseline 3 is appropriate because the schema does the heavy lifting.

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 states a specific verb and resource: 'Return datasets flagged by the latest published anomaly detection'. It also defines the ranking criterion ('ranked by how far the observed update interval exceeds its threshold'), which clearly distinguishes this from sibling tools like find_stale, find_deteriorating, and find_recovering. No ambiguity remains about what the tool returns.

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?

The description makes the intended use case clear: query pre-computed, published anomaly results rather than recomputing them ('includes pipeline-computed anomaly and reliability evidence so agents do not recompute it'). It also mentions the optional reliability filter. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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