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confortistefano

LinkedIn Ad Library MCP Server

search_ads

Search LinkedIn sponsored ads by keyword, company, or topic to analyze competitor ad copy and targeting. Returns ad type, impressions, geo-targeting, and audience insights.

Instructions

Search LinkedIn sponsored ads by keyword, company, or topic. Use to analyze competitor ad copy, messaging strategy, creative formats, geo-targeting, and audience segmentation.

Returns: advertiser name and payer, ad type (video, image, document, status update), impression ranges, country distribution (%), targeting facets (language, location, job title, company, audience), and direct link to the ad.

Example: search_ads({ keyword: "klarna", count: 25 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoResults per page (default 25, max 100)
startNoPagination offset (default 0)
keywordYesSearch keyword (company name, product, topic, or industry)
Behavior4/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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