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

Annotations already establish read-only, idempotent, closed-world, non-destructive behavior, and the description adds meaningful constraints beyond them: duplicate-ID validation, summation of separated metrics, change calculation only under a comparable baseline, and explicit exclusion of account access and automatic AI-traffic/revenue attribution. It stops short of describing error behavior when baselines are not comparable.

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?

Three compact sentences, front-loaded with the core validation/aggregation purpose, followed by constraints and limitations. Little waste, though the dense German nominal style reduces scannability slightly.

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 a two-parameter but heavily nested schema with no output schema and no field-level documentation, the description covers validation scope, aggregation behavior, and the comparison precondition reasonably well. It is still incomplete about expected output, failure modes on incomparable baselines, and the meaning of the many required metadata fields.

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% and the schema is deeply nested, so the description bears most of the burden; it does clarify the semantics of `baseline` (comparison requires comparability) and references `definitions` and duplicate `id`s. It says nothing about required fields such as `source`, `timezone`, `period_start`/`period_end`, or the `currency` pattern, leaving significant gaps.

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 names specific operations (validating exported GSC/GBP/CRM-like datasets, summing split metrics, computing changes against a baseline) rather than restating the title. It implicitly separates itself from live-account siblings via 'Kein Zugriff auf Konten', though it does not name any sibling explicitly.

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 rather than stated: 'gelieferte ... Datensätze' and 'Kein Zugriff auf Konten' signal that this tool is for offline export analysis, and the comparable-baseline condition hints at a prerequisite. However, there is no explicit when-to-use vs. when-not guidance relative to siblings like analyze_web_performance or compare_business_profiles.

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