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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the type of outputs but does not explicitly state that the tool is read-only, safe, or idempotent. For a computation tool, this is adequate but lacks explicit safety or side-effect information.

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 long, front-loaded with the core purpose, and contains no filler. Every word adds value, making it highly efficient and easy to parse.

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 tool with 8 parameters and no output schema, the description covers the high-level outputs but omits details on parameter dependency (e.g., hardware vs custom fields) and default behavior. Schema descriptions compensate partially, but the description could be more complete.

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 baseline is 3. The tool description does not add meaning beyond the schema; it repeats output types but does not explain parameter interactions (e.g., when to use hardware vs custom fields). The description offers no extra semantic value.

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 compares costs of buying, renting, or using API for running a model locally. It specifies outputs (monthly/1y/3y totals, break-even months, energy cost) and distinguishes itself from sibling tools like cheapest_hardware_for_model or compare_hardware by focusing on cost scenarios.

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 provides no guidance on when to use this tool versus alternatives like compare_hardware or recommend_hardware. It does not mention prerequisites, contexts, or when not to use it, leaving the agent to infer usage from the tool name alone.

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