linkedin validate query

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

Discloses non-obvious behavior: no network call is made, and validation surfaces both accepted and dropped fields plus the twenty-field ceiling. Annotations already cover the read-only/idempotent safety profile, so this adds real context; there is mild tension with openWorldHint=true, though that plausibly reflects the rule catalog rather than a live request.

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 tight sentences: purpose and cost-saving rationale first, return shape second. No filler and nothing buried.

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 an output schema, full param coverage and annotations, the description covers the essentials. The only omission is what a validation failure looks like versus a successful-but-truncated result, which matters for a pre-flight check.

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 pivots, metrics, dates and granularity are already documented with defaults and formats. The description only references the 'pivot and metric combination' and adds no syntax or constraint detail beyond the schema (e.g. the 1-3 pivot / 40 metric caps).

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?

States a specific verb+resource (validate a pivot/metric combination against LinkedIn reporting rules) and frames its role relative to actually spending an API call. An agent can distinguish this dry-run validator from linkedin_run_statistics_report without opening either schema.

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?

'before spending a call on it' gives a clear trigger condition for when to use this instead of the real reporting tool. It stops short of explicitly naming linkedin_run_statistics_report or stating when validation is unnecessary, so it is clear context rather than full when/when-not guidance.

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