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

With no annotations, the description carries full burden. It discloses the search scope and lists the return fields in detail (advertiser name, ad type, impression ranges, country distribution, targeting facets, direct link). This gives a good sense of the tool's behavior and output.

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 concise: three sentences covering purpose, return values, and an example. It's front-loaded and every sentence contributes value.

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?

Despite lacking an output schema and annotations, the description provides comprehensive context: purpose, use cases, return fields, and a concrete example. The only minor gap is not elaborating on pagination behavior, but that is already addressed by the 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?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds only an example invocation, which does not meaningfully enhance parameter understanding beyond what schema descriptions provide.

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 it searches LinkedIn sponsored ads by keyword, company, or topic. This is a specific verb+resource that distinguishes it from sibling tools like search_jobs and search_paid_endorsements.

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 clear context by stating it is used to analyze competitor ad copy, messaging strategy, creative formats, geo-targeting, and audience segmentation. It does not explicitly mention alternatives or exclusions, but the use case is well-defined.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly lists the returned fields (job title, organization, location, payer, description preview) and includes an example call, giving the agent a clear picture of expected behavior. However, it does not mention any side effects, safety guarantees, or rate limits, though 'search' strongly implies read-only behavior.

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 exceptionally concise and well-structured. The core purpose is front-loaded, followed by use-case context, a clear return-value list, and a concrete example. Every sentence adds value, and the example is particularly helpful for agent comprehension without being verbose.

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?

The description is complete given the tool's complexity: all parameters are documented in the schema, return values are clearly explained, and an example is supplied. The absence of an output schema is compensated by the explicit list of returned fields. Minor gaps like pagination behavior with 'start' are indirectly covered by the schema, so no major omissions exist.

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 coverage is 100%, with all three parameters (keyword, count, start) described. The description adds a useful example showing keyword and count in action, but it does not add meaning beyond the schema for the 'start' parameter or elaborate on param semantics. This meets the baseline for fully documented 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 uses a specific verb ('Search') and resource ('LinkedIn sponsored job postings'), clearly distinguishing it from sibling tools like 'search_ads' and 'search_paid_endorsements'. It also outlines concrete use cases (tracking competitor hiring patterns, team expansion, market positioning), making the tool's purpose unmistakable.

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 clear context on when to use the tool ('track competitor hiring patterns, team expansion signals, and market positioning') but does not explicitly state when not to use it or mention alternatives. Since sibling tools are listed separately, a brief note on how this differs from them would elevate this to a 5, but the current guidance is still solid.

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

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

With no annotations, the description discloses the return value (direct URLs) and gives an example, but it does not explain operational details like result ordering, rate limits, or whether this is read-only. The return information is useful, but the lack of annotations means more behavioral context would be valuable.

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 and front-loaded: one sentence for purpose, one for use cases, one for return value, and a clear example. Every sentence contributes actionable information with no 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?

For a simple search tool with fully described parameters in the schema, the description covers purpose, use cases, return output, and a concrete example. Since there is no output schema, the return description ('direct URLs to the sponsored posts') is sufficient for the agent to understand expected results.

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 three parameters already have meaningful descriptions. The tool description does not add parameter-level details beyond a single keyword example, which is minimal added value over the schema. Baseline 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 uses a specific verb ('Search') and resource ('LinkedIn thought leader ads (paid endorsements)'), which clearly defines the tool's scope and distinguishes it from the sibling search_ads and search_jobs. The parenthetical explanation and use-case list make the purpose immediately understandable.

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 explicitly states when to use it: 'identify influencer partnerships, executive branding strategies, and creator-driven campaigns by competitors.' However, it does not mention exclusions or point to alternatives like search_ads for general ad searches, so it stops short of a 5.

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