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

With no annotations, the description carries the full behavioral burden and does well: it discloses the human-confirmation gate, the anti-estimation rule, and the exact data definition (collected revenue only). It does not say what happens after submission (e.g., whether a report can be overwritten, validations that cause rejection), leaving a modest gap for a write operation.

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?

Two sentences, front-loaded with the definition and followed by the hard constraint; the closing 'Never guess' is short and high-value. No redundant restatement of the schema or title.

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?

No output schema and no annotations, so the description must define inputs and constraints — it does, for the two non-obvious parameters and the confirmation ritual. It stops short of explaining slug or post-submission behavior, which would close the remaining gap for a mutation 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?

Schema coverage is only 33%, but the description compensates for the two important parameters: it defines mrr (MRR vs. 3-month average) and mandates human_confirmed: true with its meaning. slug is never explained, though its role is inferable from the surrounding build/signup tools.

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 specific verb and resource ('Self-report monthly revenue in USD') and immediately defines the calculation rule (MRR for subscriptions, else 3-full-month average of collected revenue). No sibling tool does anything similar, so there is no ambiguity to resolve.

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?

Explicitly prescribes the workflow: ask the human for the real number, obtain explicit confirmation that it is collected revenue and not a projection, then pass human_confirmed: true. It also states an exclusion ('Never guess') and disambiguates the two revenue formulas by business model.

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