Attribution check

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered; the description adds genuinely useful behavior — it cross-references two datasets, names the specific diagnoses it performs (consent gaps, attribution model differences, missing conversion event config), and clarifies the metric semantics ('GA4 conversions are the property's key events / keyEvents'). It doesn't mention auth requirements or rate/cost constraints for the two API calls it makes.

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

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

The comparison purpose is front-loaded in the first clause, followed by diagnoses and return shape; nothing is padded or repeated. The trailing pair of documentation URLs is arguably filler for an agent that already has the tool, but it is a single compact sentence.

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

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

The tool has an output schema, so the brief mention of return contents ('both sides' totals with the discrepancy and diagnostic insights') is sufficient without enumerating fields. With 100% schema coverage and no required parameters, the definition supplies what an agent needs to call it, though it never explains how this cross-source analysis differs from validate_tracking or analyze_campaign_conversions.

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

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

Schema description coverage is 100%, with each of the five optional parameters documented (defaults, formats, the 'both dates must be set else last 30 days' rule, and conversion_events semantics), so the baseline is 3. The description adds only the tangential note that GA4 conversions are keyEvents and no direct parameter guidance beyond that.

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

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

States a specific verb and two resources ('Compare Ads-reported conversions vs GA4 — find tracking discrepancies') and enumerates the diagnostic categories it covers, so an agent can tell what it computes. It stops short of distinguishing itself from close siblings such as validate_tracking, audit_event_coverage, or analyze_campaign_conversions, which an agent browsing this large toolset would still have to disambiguate by opening schemas.

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

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

Usage is implied by the purpose ('find tracking discrepancies', 'diagnoses GDPR consent gaps'), so it is reasonably clear when an agent would reach for it. However, there is no explicit when-to-use/when-not statement and no mention of the nearly overlapping alternatives (validate_tracking, audit_event_coverage, analyze_campaign_conversions), leaving the agent to infer selection.

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