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

Beyond the annotations, the description discloses important behavior: existing dictionary entries are reused, only missing provisional nodes are created, belongs_to relationships are added, cycles are rejected, and the server independently validates structural writes. This goes well beyond what annotations or the schema could convey.

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?

Although the description is long, it is dense with decision-relevant guidance for a nuanced classification task. The most important operational instructions are front-loaded, and each subsequent sentence addresses a distinct semantic rule without unnecessary repetition.

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 single parameter, the absence of an output schema, and the conceptual complexity of subject hierarchy resolution, the description covers preconditions, input semantics, server behavior, rejection rules, and client-side validation limits. An agent has enough information to call the tool correctly.

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 only parameter, terms, is richly explained despite zero schema description coverage. The description specifies ordering (broad-to-specific), gives an example, and adds extensive semantic guidance about what counts as a distinct concept, how to handle modifiers, and why naming variants should reuse identity.

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's purpose and trigger condition: use it after vocabulary_index when a specific subject type does not yet exist. It specifies the exact input format (broad-to-specific terms) and what the server does with them, making it easy to distinguish from related resolution tools.

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?

It gives strong usage context, including when to use the tool, what to exclude ('review'), and how to handle naming variants. However, it does not explicitly name a fallback alternative tool when the subject type already exists, so the full vs-alternatives guidance is implied rather than explicit.

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