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JavaScript
Remote

Search LinkedIn ads

search_linkedin_ads
Read-only

Search LinkedIn Ad Library by company, keyword, or company ID to retrieve targeted B2B ads with real impression counts. Returns compact JSON with ad creatives, CTAs, and dates.

Instructions

Structured LinkedIn Ad Library search by company name, keyword, or companyId — use for a targeted B2B pull; use research_ads for open-ended research. Returns compact JSON {advertiser, headline, description, cta, link, media, dates, impressions} per ad — LinkedIn is the one library exposing real impression counts. Spends about a credit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNomax ads returned (1–25, default 8)
companyNoadvertiser company name
keywordNokeyword across all advertisers
companyIdNoLinkedIn company id (numeric) when the name is ambiguous
countriesNoCSV of 2-letter codes like 'US,CA'; omit or 'ALL' = worldwide

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.161
    • changedOutput schema / (root)
      Previous value: -{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "additionalProperties": false,
-  "properties": {
-    "ads": {
-      "description": "the compact ad objects ({advertiser, headline, description, cta, link, media, dates, impressions})",
-      "items": {},
-      "type": "array"
-    },
-    "found": {
-      "description": "total ads found upstream",
-      "type": "number"
-    }
-  },
-  "type": "object"
-}New value: +null
  2. Addedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark it read-only and non-destructive, and the description adds meaningful context: it returns compact JSON with specific fields, notes that LinkedIn exposes real impression counts, and mentions the credit cost. This goes beyond the annotations and helps set expectations, though it stops short of detailing pagination or rate limits.

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?

Three compact sentences deliver purpose, usage guidance, return shape, a differentiator, and cost. Every sentence earns its place and the key purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by enumerating the returned JSON fields. It also covers the distinguishing impression data and credit cost, making it complete enough for an agent to invoke the tool correctly with just the schema and description.

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 baseline is 3. The description reinforces the three search modes (company, keyword, companyId) and the impression-count highlight, but it does not significantly add param semantics beyond what the schema already documents.

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 a specific verb and resource: a structured LinkedIn Ad Library search by company name, keyword, or companyId. It also differentiates itself from the sibling tool research_ads by framing this as a targeted B2B pull rather than open-ended research.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly tells when to use this tool versus the key alternative: use it for targeted B2B pull and research_ads for open-ended research. This gives an agent clear selection criteria without needing to inspect schemas.

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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