Metrics split by a dimension (country, device, placement, age, gender, hour), across platforms

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds only the HTTP endpoint (POST /v1/analytics/breakdown), which is marginal for an agent, and says nothing about how results are grouped, the 500-row limit behavior, currency handling, or whether platforms default to all connected accounts.

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?

A single front-loaded sentence with no filler; the scope (dimension) and endpoint come first. Its brevity is under-specification rather than verbosity, so it loses a point only because the space saved was not reallocated to useful detail.

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?

For a read-only analytics query with no output schema, the definition is minimally viable: an agent knows roughly what it returns and that a dimension is required. But with a deeply nested body, seven optional fields, and a huge platforms enum all undocumented, an agent cannot call this correctly without reading the schema closely.

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 carries the full burden, yet it only touches the 'dimension' field and omits from/to, limit, clientId, currency, platforms, and byPlatform entirely. The dimension list it does give is incomplete (no 'creative' or 'ad') and uses 'country' where the schema requires 'geo_country', which could actively mislead.

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 concrete operation (metrics split by a dimension) and enumerates example dimensions, so an agent can tell this is a dimensional breakdown/grouping query. It does not distinguish itself from siblings like analytics_timeseries or analytics_compare, and its dimension list ('country', plus omitted 'creative'/'ad') only loosely matches the enum ('geo_country'), so it stops short of a 5.

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?

There is no guidance on when to pick this over analytics_timeseries, analytics_compare, analytics_pacing, or analytics_digest, and no mention of prerequisites such as a connected platform or clientId. The reader must infer the use case entirely from the name and the dimension list.

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