context_window

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

With no annotations, the description carries the full behavioral burden. It explains that all parameters are optional, that defaults mirror an interactive calculator, and that the response includes result, formula, interpretation, and a cite_as sentence. This gives agents a solid understanding of behavior and response contents.

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 compact and front-loaded with the core purpose. Every sentence contributes useful information: the questions answered, the default behavior, the reference calculator, and the response contents.

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 description covers purpose, defaults, response contents, and provides a reference link. It is sufficient for a low-risk optional-parameter calculator, though it could be stronger if it distinguished this tool from closely related siblings like token_cost.

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 every parameter and default. The description reaffirms that all parameters are optional and points to the calculator defaults, but it adds little meaning beyond the schema.

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 what the tool computes: token count, whether content fits the window, and per-request cost. It frames the purpose as concrete questions rather than a tautology, though it does not explicitly differentiate itself from sibling cost/token tools.

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 opening questions provide clear context for when to use the tool. It does not name alternatives or state exclusions, but the intended use case is evident from the questions and parameter descriptions.

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