LinkedIn Ads MCP Server

linkedin get demographics

linkedin_get_demographics

Read-onlyIdempotent

Break down LinkedIn ad delivery by professional demographics—company, size, industry, seniority, job title, function, or location—and see stated limits alongside each row.

Instructions

Break LinkedIn delivery down by professional demographics: company, company size, industry, seniority, job title, job function or location. These are the numbers LinkedIn is bought for, and they come with limits the tool states alongside the rows.

Input Schema

Name	Required	Description
`limit`	No	Keep only the top rows by impressions. Defaults to 50.
`endDate`	No	Inclusive end, YYYY-MM-DD. Defaults to today.
`metrics`	No	Defaults to spend, impressions, clicks and CTR. Conversion value, carousel and reach fields are not available here.
`breakdown`	Yes	Which professional demographic to group by.
`startDate`	Yes	Inclusive start, YYYY-MM-DD.
`adAccountId`	Yes	Selected LinkedIn advertising account, numeric ID. It must be listed in LINKEDIN_ADS_ACCOUNT_IDS.
`campaignIds`	No	Restrict reporting to these campaign IDs or URNs in the selected account.
`resolveNames`	No	Resolve the returned URNs to readable names. On by default.
`campaignGroupIds`	No	Restrict reporting to these owned campaign-group IDs.

Output Schema

Name	Required	Description	Default
`result`	Yes	Original tool result: parsed JSON when the text is JSON, otherwise the text. Provider fields depend on the selected query.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 23, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered. The description does add one piece of behavioral context beyond annotations — that results come with LinkedIn's limits surfaced alongside the rows — but it doesn't specify which limits, row caps, or latency, so it remains modest added value.

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?

Two sentences, purpose front-loaded ahead of the caveat about limits. The middle clause ('the numbers LinkedIn is bought for') is marketing filler that doesn't earn its place, but overall it is tight and well-ordered.

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?

With an output schema, full parameter descriptions, and annotations covering the safety profile, the description only needs to convey purpose and scope — which it does. The one residual gap is not routing the agent away from general-insights siblings, but that is minor given the richness of the surrounding structured data.

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 every parameter (breakdown enum, metrics defaults, limit, campaignIds, resolveNames) is already documented in the schema. The description only restates the breakdown dimension list, adding no syntax, default behavior, or edge cases beyond the structured fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('break down') and resource ('LinkedIn delivery by professional demographics') and enumerates the supported dimensions, which matches the breakdown enum. An agent can tell what it returns, though it never names the closest sibling (get_insights / run_statistics_report) to differentiate the demographic axis from general reporting.

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

Usage Guidelines3/5

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

Usage context is implied by the framing ('these are the numbers LinkedIn is bought for'), which signals audience/demographic analysis, but there is no explicit when-to-use versus linkedin_get_insights or linkedin_run_statistics_report, and no stated prerequisites beyond what the schema requires.

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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