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

Goes well beyond the annotations (readOnly=false, idempotent=false, destructive=false) by disclosing a quota (15 topics per brand), an authorization constraint (workspace admins only), a required user confirmation, the post-add behavior (Parse starts collecting answers), and an explicit prompt-injection defense for quotedMaterial fields.

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?

Front-loads the usage condition and follows with effect, limits, and prerequisites; every sentence carries information. The injection warning adds length but earns its place for a write tool.

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 an output schema present, return values need not be explained. The description covers purpose, quotas, permissions, confirmation gating, and the safety caveat, leaving nothing an agent needs to invoke it 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?

Schema description coverage is 100%, so both parameters are already documented. The description reinforces that the question is used verbatim ('Parse asks it in exactly these words'), but that nuance is already present in the schema, so it adds little beyond the baseline.

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 specific scenario (tracking something buyers ask AI) and the concrete effect (Parse asks the question verbatim and counts it in the brand's Custom topics). It is clearly an add-topic tool, distinguishable from its inverse sibling parse_remove_topic, though it never names that sibling.

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?

Gives a clear triggering condition ('Use when the user wants Parse to track something buyers ask AI') plus prerequisites: workspace admins only and 'Call only after the user confirms.' It lacks explicit exclusions or named alternatives, but no true alternative tool exists here, so the guidance is strong.

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