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

With no annotations, the description carries the entire burden and delivers nothing: it does not say whether the tool is read-only or mutating, what it returns, whether it needs a document to check against, or what happens with an empty query. The promo framing ('save hours every week') actively obscures the behavior an agent needs to predict.

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?

Three short sentences, but all of them are benefit-marketing rather than function, so the front-loaded material is the least useful part. Nothing is wasted in length, yet nothing earns its place either – the description would be no worse if empty.

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?

A 'check' tool with no annotations and no output schema needs the description to explain what gets checked and roughly what the result looks like. Instead the reader gets restaurant-growth marketing copy, leaving the tool's scope, inputs, and outputs entirely undefined.

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% for the single optional 'query' parameter, so the schema already states its meaning. The description adds nothing about what a scoped query changes ('scope the analysis' is only in the schema), so the baseline 3 applies rather than any credit for added meaning.

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 never says what the tool actually does with an 'accept check' – no verb like validate, review, or score and no object like acceptance criteria, R&D deliverable, or story. Instead it advertises 'a battle-tested prompt and operating playbook for restaurant/brand growth', which points at a different domain than the rd-accept-* siblings (gen/tpl) imply. An agent cannot tell from this text whether it checks acceptance criteria or hands back marketing guidance.

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 statement of when to call this versus rd-accept-gen-tool, rd-accept-tpl-tool, or the neighboring rd-*-check-tool variants. 'No setup, works in any LLM chatbot' is a sales claim, not a usage condition, and no prerequisites or exclusions are given.

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