analyze_campaign_conversions

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds behavioral insights like revealing click-to-session ratios as a GDPR indicator and computing cost-per-GA4-conversion. It does not contradict annotations and provides useful context beyond the annotations, such as date format and default behavior.

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 description is relatively concise, with five sentences covering the core conversion mapping, key outputs, and date format. It is front-loaded with the main purpose and avoids unnecessary details. Every sentence adds value, though the structure could be slightly improved by grouping related concepts (e.g., output details).

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?

Given the tool's complexity (5 parameters, output schema exists), the description covers the main purpose and several key outputs (click-to-session ratios, cost-per-GA4-conversion, non-paid conversion rates). It does not need to explain return values due to output schema. However, it could be more complete by mentioning default behaviors for parameters other than dates and required permissions.

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 0%, so the description must compensate. It only explains date_format for the two date parameters (e.g., 'YYYY-MM-DD', empty = last 30 days). It does not describe customer_id, property_id, or campaign_name, leaving ambiguity about their purpose. This is insufficient for a 5-parameter tool with no parameter descriptions elsewhere.

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?

The description clearly states the tool maps Google Ads campaign clicks to GA4 conversions and computes cost-per-GA4-conversion, click-to-session ratios, and non-paid conversion rates. It distinguishes itself from sibling tools like get_campaign_performance (which only retrieves campaign metrics) and attribution_check (which may focus on attribution models) by specifying cross-platform integration and unique outputs.

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?

The description provides clear context on when to use the tool (for analyzing click-to-conversion mapping, GDPR indicators, and comparing Ads vs. GA4 conversions). It mentions non-paid channel rates for context but does not explicitly state when not to use it or suggest alternatives. This is adequate but lacks formal exclusions.

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