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linkedin-ads-mcp

compare_performance

Compare LinkedIn ad performance across two time periods, campaigns, or campaign groups. Calculates percentage changes and highlights significant differences for reporting.

Instructions

Compares performance between two time periods, campaigns, or campaign groups. Calculates percentage changes and highlights significant differences. Essential for reporting on performance trends.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodAYesFirst period or entity set for comparison
periodBYesSecond period or entity set for comparison
accountIdYesThe LinkedIn Ad Account ID
comparisonTypeYesType of comparison to make

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It usefully discloses that the tool calculates percentage changes and highlights significant differences, but it does not mention read-only nature, required permissions, output shape, or how significance is determined.

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 concise sentences, each contributing information: what it compares, what it calculates, and when it is useful. No filler or repetition of schema content.

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?

Given the tool's moderate complexity, full schema coverage, and absence of annotations, the description covers the conceptual operation well. It gives enough of an output expectation through 'percentage changes' and 'significant differences,' though an explicit output shape would make it fully complete.

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 the schema already documents all parameters. The description adds a high-level mapping between comparison scopes and the comparisonType enum, but it does not add details beyond what the schema already provides.

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 uses a specific verb ('compares') with a clear resource ('performance') and explicitly names the three comparison scopes: time periods, campaigns, or campaign groups. This makes it easy to distinguish from single-period performance getters like get_campaign_performance or get_creative_performance.

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

Usage Guidelines4/5

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

The phrase 'Essential for reporting on performance trends' gives clear context for when to use the tool. However, it does not explicitly state when not to use it or point to alternative tools for single-period performance checks.

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