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

Annotations already cover read-only, idempotent, non-destructive, and closed-world behavior. The description adds useful behavioral context beyond that: one action type per call, one point per month, and optional campaign or LinkedIn-account scoping. It still does not describe the exact aggregation semantics or return fields, but it adds meaningful clarity for this read-only analytics tool.

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 tightly written sentences, front-loaded with the core purpose and followed by the key alternative. There is no wasted wording.

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 simple read-only analytics tool with rich annotations and full parameter schema coverage, the description is nearly complete. It conveys aggregation level and scoping, though it leaves the exact returned metric fields unstated.

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 parameter meanings are already fully documented in the schema. The description reinforces the one-action-type constraint and optional campaign/account scoping, but adds little syntax or format detail beyond the schema.

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 resource and shape: month-over-month totals for one action type, optionally scoped to a campaign or LinkedIn account. It also distinguishes the tool from `analytics_reporting` by contrasting monthly one-action-type trends with daily all-action-type reporting.

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 clearly signals the intended use case: monthly trend analysis for a single action type, with an explicit alternative for daily granularity across all action types. It does not explicitly distinguish itself from other analytics siblings such as `analytics_campaign` or `analytics_account`, so it falls short of 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.