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

Beyond annotations, the description adds meaningful behavioral context: it costs 1 credit, targets either the founder's idea or a top bet, and returns a table with domain, availability, price, and companion domains. It does not fully describe side effects or persistence, but the annotations already indicate the operation is not read-only.

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 concise and front-loaded: core action first, then target semantics, then cost and output. Every sentence carries useful information with no filler.

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?

For a two-parameter tool with no output schema, the description is complete enough for an agent to invoke correctly. It covers prerequisites, parameter defaults and options, cost, and the structure of the returned table.

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 for parameters is 100%, so the baseline is 3. The description adds value by explaining the default target ('idea'), what bet-1/bet-2/bet-3 mean, and the requirement that research be complete, going beyond the schema descriptions.

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 verb and resource: 'Generate a set of brandable names for a completed research.' It also distinguishes the tool from siblings like listing, deleting, or suggesting more names by emphasizing grounded positioning and domain-availability checking.

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 gives clear usage context: requires the parent research to be complete and explains how the target parameter selects what to name. It does not explicitly name alternatives or exclusion conditions, so it stops short of a 5, but an agent can infer the correct use case.

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