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zackscriven

ghl-mcp-server-v2

by zackscriven

ghl_ad_li_targeting_search

Read-onlyIdempotent

Search LinkedIn ad targeting by facet to discover locations, industries, and job titles for precise ad campaign targeting.

Instructions

Search targeting options Search LinkedIn targeting facets such as locations, industries, and job titles Endpoint: GET /ad-publishing/linkedin/targeting/search (Version header: 2021-07-28; source: v3/ad-publishing-v3.json) OAuth scopes: adPublishing.readonly

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoQuery parameter
facetYesTargeting facet
queryNoSearch query
locationIdYesLocation identifier
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by noting OAuth scopes (adPublishing.readonly), endpoint details, and version header. This provides useful behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with three distinct parts: brief title, specific purpose sentence, and technical details (endpoint, version, scopes). No redundant information, though the first line ('Search targeting options') partly repeats the annotation title.

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

Completeness2/5

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

No output schema is provided, and the description does not explain what the search returns (e.g., list of matching targeting options, pagination hints). For a search tool, this omission leaves the agent uncertain about the response structure, warranting a lower score.

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 coverage is 100%, so the input schema already documents all four parameters with descriptions and examples. The description does not add additional meaning per parameter beyond listing example facets (e.g., locations, industries). Meets the baseline for high schema coverage.

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 the tool searches LinkedIn targeting facets like locations, industries, and job titles. The verb 'search' and resource 'LinkedIn targeting options' are specific, and the mention of LinkedIn distinguishes it from sibling tools (e.g., ghl_ad_fb_targeting_search, ghl_ad_google_targeting_search).

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like Facebook or Google targeting search. The description does not mention when not to use it or provide context for selection. It merely states what it does.

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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