usage_platform_adjacency

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

With no annotations, this description takes full responsibility for behavioral disclosure. It explicitly states aggregation across all customers, excludes ('internal, static, and console traffic'), computes on lifetime successful-only requests, and is cached at most once per 24 hours. These are meaningful details that would materially affect an agent's expectations about data freshness and scope.

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 ~50 words and front-loads the core action and output. The middle sentence about aggregation details is dense but earns its place; the final sentence adds use case context. It's slightly dense but not bloated.

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 there are no parameters, no output schema, and no annotations, this description covers exactly what a caller needs to know to use the tool: what it returns, the data freshness window, traffic exclusions, and the intended dashboard use case. There is no material gap for a zero-argument read-only aggregation tool.

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?

The parameter schema is empty and the description doesn't add any parameter semantics, but for a 0-parameter tool the baseline is 4. The description's talk of 'per scraping platform' and 'top entry endpoints' is about output, not input, so no instrinsic parameter 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 clearly identifies the resource (cross-platform user reach and co-usage), names the aggregation dimension (per scraping platform), and enumerates the returned fields (user counts, request volume, entry endpoints, co-usage platforms). This distinguishes it from generic usage_overview or usage_endpoints tools, even without naming them.

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 gives a concrete use case—'Powers the console dashboard's Explore more recommendation'—which implies when to use it (recommendation/exploration contexts). It doesn't explicitly contrast against sibling usage_* tools or state when not to use it, so it falls short of a full when/when-not guide.

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