Suggest practical AI use-cases for a business

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

With no annotations provided, the description carries the full burden. It mentions the output contains impact and effort ratings, but does not disclose whether results are static or dynamically generated, any dependency on external data, or rate limits. Since the tool is a straightforward suggestion generator, the missing details are acceptable but not exceptional.

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 description is two sentences with zero waste. It front-loads the core functionality and usage examples. Every line adds value.

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?

The tool has three fully documented parameters and no output schema. The description complements this by stating the return format (impact and effort rating). For a simple suggestion tool, this is sufficient. One could argue it's complete, but a small gap is not describing the structure of the returned data beyond ratings.

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 the schema already documents each parameter. The description does not add additional meaning to the parameters beyond what the schema provides. It adds value by describing the output format (impact and effort rating), but that is separate from parameter semantics.

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 returns concrete AI/automation use-cases with impact and effort ratings. The title reinforces this. It also provides explicit example queries ('what could AI do for my business?'), making the purpose unmistakable and well-differentiated from siblings (which are about assessment, consultation, and services).

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?

The description explicitly states when to use the tool: 'Use when someone asks...' and lists three types of queries. This is direct guidance for an AI agent. Though it doesn't mention when not to use, the sibling tools cover different scenarios, so no exclusion is needed.

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