Search LinkedIn ads

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

Annotations already cover readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds valuable context: the return format (compact JSON with specific fields), the unique advantage (LinkedIn exposes real impression counts), and a cost warning ('Spends about a credit'). This goes beyond what annotations provide without contradicting them.

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 two sentences with zero filler. The core purpose is front-loaded, followed by the usage distinction, then the return format and unique value proposition. Every sentence earns its place and the structure is optimal for quick agent parsing.

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 the tool has annotations covering safety, a fully documented schema, and a brief description of the output format (compact JSON with fields), the description covers the essentials. It does not mention pagination or default ordering, but these are not critical for a search tool and the schema covers the limit parameter. It is sufficiently complete for an agent to invoke it correctly.

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 has 100% coverage with descriptions for all 5 parameters (limit, company, keyword, companyId, countries), so the baseline is 3. The description only mentions three of the parameters (company name, keyword, companyId) and adds no additional syntax or format details beyond what the schema already documents, so it does not elevate above 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 clearly states the exact action (structured LinkedIn Ad Library search) and the searchable fields (company name, keyword, or companyId). It also explicitly differentiates itself from the sibling tool research_ads by framing this as 'targeted B2B pull' versus 'open-ended research', so an agent can easily decide which to use.

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 gives a direct when-to-use instruction ('use for a targeted B2B pull') and explicitly names the alternative tool ('use research_ads for open-ended research'). This leaves no ambiguity about the context for this tool versus its sibling.

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