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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds the approximation method (cl100k_base, ~4 chars/token), which sets expectations about accuracy, and discloses the return fields. This adds meaningful behavioral context beyond the annotations without contradicting them.

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 no filler. The first sentence states the action and method; the second provides usage timing and output summary. Every word earns its place.

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 is simple with one parameter and an output schema. The description covers purpose, usage timing, approximation details, and return fields, making it complete for an agent to select and invoke correctly. The existing output schema handles return value schemas, so no additional return documentation is needed.

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?

The schema covers 100% of the parameter with a basic description. The tool description adds no additional detail about the input parameter (e.g., length limits or encoding), so it does not exceed the baseline for a fully covered 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 it estimates token count for a text string using the cl100k_base approximation. It distinguishes itself from siblings like estimate_llm_cost by focusing on token counting rather than cost, and mentions the return fields (token estimate, character count, word count) which further clarifies its function.

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 instructs to call it before sending text to an LLM API to check context window fit and estimate cost. This provides clear when-to-use context, but it does not mention alternative tools (e.g., truncate_to_tokens) or exclusions, so it lacks explicit alternatives.

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