LinkedIn Ads MCP Server

linkedin run statistics report

linkedin_run_statistics_report

Read-onlyIdempotent

Generate LinkedIn Ads performance reports grouped by up to three pivots to cross campaign with device or campaign group with serving location in one request.

Instructions

Run a LinkedIn Ads report grouped by up to three pivots at once. This is the statistics finder, the only way to cross campaign with device, or campaign group with serving location, in a single request.

Input Schema

Name	Required	Description
`pivots`	Yes	One to three pivots. Order decides the order of pivotValues.
`endDate`	No	Inclusive end, YYYY-MM-DD. Defaults to today.
`metrics`	No	Metric keys from linkedin://metrics. Defaults to spend, impressions, clicks and the ratios.
`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.
`creativeIds`	No	Restrict reporting to these owned creative IDs.
`campaignType`	No	Native campaign format filter.
`objectiveType`	No	Native campaign objective filter.
`timeGranularity`	No	Native reporting aggregation interval. Defaults to ALL.
`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

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds only the three-pivot ceiling, which the schema also states, and says nothing about the outbound API call behavior, payload size, or latency that would matter for a wide report.

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?

Two tight sentences, capability stated first and the differentiator second. Nothing redundant or padded; every clause carries information.

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

Completeness3/5

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

The tool is read-only, has an output schema, and has fully described parameters, so the burden is light. However, for an 11-parameter reporting tool the description never mentions the required trio (adAccountId, pivots, startDate) or the default behavior for metrics and time granularity, leaving the agent to open the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description earns extra credit by naming concrete pivot values ('campaign', 'device', 'campaign group', 'serving location'). That is genuinely useful since 'pivots' is an unconstrained string array with no enum in the schema.

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 and resource: run a LinkedIn Ads report grouped by up to three pivots. The 'only way to cross campaign with device, or campaign group with serving location' phrasing functionally differentiates it from linkedin_get_insights and the other getters, though no sibling is named explicitly.

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?

It implies when to reach for it (multi-pivot cross-tabulation) but never states when not to, nor names linkedin_get_insights or another sibling as the simpler alternative for single-dimension pulls. Usage is inferable rather than directed.

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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