Identify Freshness and Schema Risks

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 only needs to add selection behavior. It does so by expanding beyond the name: statuses aging/stale/degraded and the subtle inclusion of datasets missing from the snapshot. No auth, rate-limit, or side-effect warnings are needed for a read-only list.

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 sentences, front-loaded with the result behavior and followed by a direct usage condition. There is no verbosity or redundant restating of the name/title.

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?

For a tool with one optional parameter, full schema coverage, an output schema, and rich annotations, the description is nearly complete. The only notable gap is positioning against the large set of sibling tools, but the explicit use case mitigates that.

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?

The input schema fully documents max_age_hours with default, minimum, examples, and a description, so the description adds little parameter-level meaning. With 100% schema coverage, the baseline of 3 applies.

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') with explicit inclusion criteria: status aging/stale/degraded plus datasets missing from the latest health snapshot. This goes beyond the name/title, but it does not explicitly distinguish itself from closely named siblings like find_schema_drift or find_unreliable.

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 provides a clear 'Use when an agent needs to know which data has a freshness or schema-validity risk' statement, which gives an agent actionable context. It stops short of naming alternatives or saying when not to use it, so it lacks explicit exclusions.

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