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adops-tool

LinkedIn Ads MCP Toolkit

by adops-tool

get_campaign_performance

Read-onlyIdempotent

Retrieve campaign performance metrics like impressions, clicks, spend, CTR, and conversions within a date range to monitor and optimize your LinkedIn ad campaigns daily.

Instructions

Retrieves performance metrics for campaigns within a specified date range. Returns key metrics like impressions, clicks, spend, CTR, conversions, audience penetration, and average dwell time. The primary tool for daily campaign monitoring and optimization decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endDateNoEnd date in YYYY-MM-DD format. Default: today
metricsNoOptional LinkedIn metric names. Missing standard metrics are not available in the response; use get_analytics for raw custom metrics.
accountIdYesThe LinkedIn Ad Account ID
startDateYesStart date in YYYY-MM-DD format
campaignIdsNoSpecific campaign IDs to filter. If omitted, returns all campaigns.
timeGranularityNoTime granularity for the data. Default: ALL
campaignGroupIdsNoFilter by campaign group IDs

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive, so the safety profile is fully covered. The description adds the metric list returned, which is useful behavioral context given the absence of an output schema, but says nothing about rate limits, auth requirements, or response shape.

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 sentences: verb+resource first, return contents second, use case third. No filler, every sentence carries distinct information, and the actionable purpose is front-loaded.

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 no output schema, the description compensates by listing the key metrics returned, and the schema fully documents all 7 parameters. It is nearly complete; a brief note on granularity/pagination behavior or how it relates to sibling performance tools would close the remaining gap.

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 each of the 7 parameters is documented in the schema itself, establishing a baseline of 3. The description adds no parameter-level detail (formats, filter behavior) beyond what the schema already states.

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 ('Retrieves performance metrics for campaigns') and enumerates the metrics returned, so the agent knows exactly what it gets. However, it does not differentiate itself from the many performance siblings (get_creative_performance, get_conversion_performance, get_daily_trends, compare_performance) beyond claiming to be the 'primary' tool.

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?

'The primary tool for daily campaign monitoring and optimization decisions' implies a use case, which is better than nothing. But it names no alternatives and gives no when-not guidance, even though the sibling set contains several overlapping performance tools. The only alternative hint ('use get_analytics for raw custom metrics') lives in the schema, not the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.