get_token_count

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action without any detail on what the tool returns (e.g., a single number, per-text counts), how the model parameter affects behavior, error scenarios, or rate limits. This leaves substantial ambiguity for an agent invoking the tool.

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 a single, compact sentence that front-loads the core purpose. It has zero filler and is easy to parse. However, it is so minimal that it omits relevant context (like return format), which slightly reduces its usefulness despite the concise structure.

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 no output schema and no annotations, the description should explain what the caller gets back and any important caveats. It does not mention the return value (a number? per-item counts?), whether the 'model' parameter changes tokenization, or any limitations. With only two parameters, the description is incomplete for an agent to call it confidently.

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 is 100% – both 'input' and 'model' have descriptions in the schema. The description adds no information beyond what the schema already provides; it merely restates that it counts tokens for the input array. Per the guideline, a baseline of 3 is appropriate when the schema does the heavy lifting, and no additional semantic value is needed here.

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 states a specific verb ('подсчёт' = counting) and resource ('токенов' = tokens) and the input type (array of texts), making the purpose clear. It does not explicitly differentiate from sibling tools, but the siblings (chat, embed_text, generate_image, etc.) are obviously different in function, so an agent can distinguish them without additional guidance. One point off for not naming the alternative or the model-specific nature.

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?

There is no guidance on when to use this tool versus any other. The description simply says it counts tokens for an array of texts, with no mention of when it's appropriate, what alternatives exist (e.g., chat for generation, embed_text for embeddings), or any prerequisites. An agent must infer usage from the name and schema alone.

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