linkedin_ad_targeting

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

With no annotations provided, the description carries the full behavioral burden. 'Search' clearly indicates a read-only kind of operation and the facet list gives some behavioral context. However, it does not disclose authentication requirements, rate limits, or any other behavioral cautions; the presence of an output schema partially reduces the need to describe return values.

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 a single, front-loaded sentence with no filler. It efficiently communicates the tool's domain and purpose in a way that an agent can parse quickly.

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 straightforward search/facet tool with four documented parameters and an output schema present, the description plus schema provides enough context for correct invocation. It could be slightly stronger with an explicit note about which facet values are supported or what the search returns, but those are already partially covered by the schema and output schema.

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?

The input schema already provides 100% coverage for all four parameters, so the description does not need to compensate. The description adds example facet types, but it also introduces 'company sizes' which is not listed in the schema's facet description, adding limited and possibly confusing semantic value. Thus it sits at the baseline.

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 uses a specific verb ('Search') and a clear resource ('LinkedIn ad targeting facets'), and lists example facet categories. It distinguishes itself from sibling platform-specific tools by naming LinkedIn explicitly. It loses a point because the description mentions 'company sizes' while the input schema's facet description lists 'seniorities' instead, creating minor ambiguity.

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 implies usage for LinkedIn ad targeting research, and sibling names like meta_interest_targeting and tiktok_interest_targeting suggest platform-specific alternatives. However, it does not explicitly state when to prefer this tool over those alternatives or when not to use it, so the guidance is implied rather than stated.

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