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Hermoso

Hermoso

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by hermoso-ai
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JavaScript
Remote

Search LinkedIn ads

search_linkedin_ads

Search LinkedIn Ad Library by company, keyword, or company ID for targeted B2B pulls. Returns compact JSON with advertiser, headline, CTA, links, dates, and impressions.

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's library includes impression counts. Spends about a credit.

Input 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 cover the safety profile (destructiveHint=false, openWorldHint=true) but do not convey cost. The description adds 'Spends about a credit', which is real behavioral context an agent needs before calling. It also discloses the return shape, though it doesn't explain pagination or what limits real results.

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 tight clauses: purpose, routing alternative, return shape and cost. Purpose and the sibling disambiguation are front-loaded, and every sentence carries distinct information with no filler.

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?

For a 5-param read tool with no output schema, the description supplies the otherwise-missing return fields, notes the LinkedIn-specific impression data, discloses credit cost, and routes to the sibling tool. Everything an agent needs to select and invoke it is present.

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 all five parameters (limit, company, keyword, companyId, countries) are already documented. The description only echoes the three lookup keys already in the schema and adds no format or constraint detail beyond it, so the baseline of 3 applies.

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?

States a specific verb and resource ('Structured LinkedIn Ad Library search') plus the three supported lookup keys (company name, keyword, companyId). It explicitly distinguishes itself from the sibling research_ads, so an agent can disambiguate without opening either schema.

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?

'use for a targeted B2B pull; use research_ads for open-ended research' gives an explicit when-to-use and names the alternative with the condition that selects it. Nothing is left to inference about routing.

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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