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

Read-only behavior is already covered by annotations, but the description adds meaningful context beyond them: it explains that fill rate alone is misleading and that separating power is reported because a field present on every piece with one distinct value carries no information. It also exposes the relevant capability and npm mirror, which clarifies what the tool computes.

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 core description is compact: two sentences carry the purpose and the key analytic nuance, followed by useful capability/mirror metadata. The opening phrase is slightly abstract, but the second sentence grounds it immediately. No unnecessary prose.

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?

With one optional parameter, fully described schema, and an output schema, the description plus structured data cover the main call requirements. It tells the agent what is measured, why the measurement matters, and that the operation is read-only. It only lacks explicit sibling routing, which is already penalized in usage_guidelines.

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 input schema already fully documents the single optional property parameter, so the baseline is 3. The description reinforces that reporting is per property but adds no new format, constraints, or relation to dimensions beyond what the schema already provides.

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 states a concrete diagnostic purpose: whether a magazine's corpus can support adaptive promise, then specifies that it reports each relational dimension's fill and separating power per property. This is specific enough to identify the tool, but it does not explicitly differentiate it from sibling tools like sequence_path or corpus.query.

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 explicit guidance on when to use this tool versus alternatives. The opening clause about adaptive promise implies a diagnostic use case, but the description never names sibling tools or states why relation_floor is preferred over them. An agent is left to infer usage from context.

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