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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the core safety profile is covered. The description adds valuable behavioral context beyond annotations: it specifies the exact JSON return structure, highlights the unique value (real impression counts), and mentions the credit cost. It doesn't go into rate limits or pagination, but given annotation coverage, this is a strong addition.

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?

The description is compact—two sentences—and efficiently front-loads the core purpose before providing usage guidance and output details. Every sentence earns its place, with no redundancy or filler.

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 there is no output schema, the description compensates by specifying the exact JSON fields returned. It also covers when to use it, its unique data advantage, and cost. With annotations handling safety, nothing an agent needs to call this tool correctly is missing.

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%, and the parameter descriptions are already informative (e.g., limit range, companyId usage, countries format). The tool description does not add much meaning beyond the schema; it merely reiterates that search can be by company, keyword, or companyId. Since the schema fully documents parameters, the baseline of 3 is appropriate.

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 clearly states the tool performs a structured search of the LinkedIn Ad Library by company name, keyword, or companyId. It also distinguishes this tool from research_ads by explicitly naming the alternative and its intended use case (targeted B2B pull vs. open-ended research), making sibling differentiation explicit.

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 description provides direct usage guidance: 'use for a targeted B2B pull; use research_ads for open-ended research.' This tells an agent exactly when to select this tool over a sibling. It also adds context about the unique value of impression counts and the credit cost, further informing selection.

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