get_analytics

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

Annotations already cover readOnly/openWorld/idempotent/destructive, so the safety profile is settled. The description adds a genuinely non-obvious operational behavior — that large metric selections are 'split and merged automatically', meaning results may be fetched via internal fan-out — which matters for latency and limit expectations and is not derivable from the schema or annotations.

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 with zero filler. The core capability is front-loaded in sentence one, and the auto-split/merge caveat follows immediately, earning its place by changing expectations about result assembly.

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?

There is no output schema, so return values need not be described, and annotations cover the safety profile. What remains missing for a 9-parameter, 0%-coverage query tool with demographic pivots and date parameters is format/permission guidance — accepted date strings, account scoping, and the practical meaning of the demographic pivot enum values.

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 0% across 9 parameters, so the description carries the burden and only partially does so: it names the metric, pivot, filter and granularity concepts but never explains date formats, ID formats, the 100-metric/500-entity caps, or what the 13 pivot enum values mean. It gives category-level mapping to the schema, not the format-level detail a 0%-coverage schema lacks.

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 (Query) and resource (raw LinkedIn analytics) and enumerates the dimensions of control: custom metrics, entity or demographic pivots, filters, time granularity. This implicitly distinguishes it from the fixed-shape siblings (get_campaign_performance, get_daily_trends), but it never names an alternative, so sibling differentiation is left to inference.

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 phrase 'custom metrics, entity or demographic pivots' implies this is the flexible/generic query tool to reach for when the prebuilt reports won't do, which is real implied guidance. However there is no explicit when/when-not statement and no mention of which sibling report tools it supersedes, so an agent must infer the routing.

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