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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context beyond annotations: it is free (not counted against tasks), defines the comparison window behavior, notes LinkedIn ad accounts carry no reporting timezone (all UTC days), explains typical B2B volume noise, and warns not to misread CPC rises or zero-lead campaigns. This is rich, non-redundant context.

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?

Well-structured with a clear READ-ONLY banner, metric list, use/don't-use routing, and behavioral notes. Each sentence earns its place by providing unique guidance. Slightly verbose (multiple do/don't blocks and repetition of baseline concept), but front-loaded and scannable.

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 9 optional parameters, no output schema, and complex reporting semantics, the description covers routing, defaults, timezone handling, and diagnostic follow-ups. It stops short of explaining the exact return structure (though no output schema exists, so some return-format disclosure would help). Nonetheless, it is complete enough for correct invocation.

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 all nine parameters are fully documented in the schema itself (including defaults, enum values, date pairing rules, and the ad_account_id string format). The description mostly reinforces parameter semantics with usage-level guidance (default to last_30_days if unspecified, use last_7_days minimum, check conversions on zero leads) rather than adding syntax beyond the schema. Baseline 3 is appropriate when 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?

States a specific verb+resource (reports LinkedIn campaign performance) and enumerates the metrics returned (spend, impressions, clicks, CTR, CPC, CPM, leads, cost per lead, conversions). It distinguishes itself from siblings by naming generate_report_now and the list tools as alternatives. An agent can identify the tool's role without opening the 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?

Explicit when-to-use ('user asks how LinkedIn is doing', 'before recommending any budget or bid change'), explicit when-not ('not for a brief or report to keep/forward — that is generate_report_now'), and directs alternative workflow ('start here, not with a list tool'). Also gives a default window recommendation and a diagnostic follow-up (linkedin_list_conversions). This is exemplary routing guidance.

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