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

With no annotations, the description carries responsibility for behavioral disclosure. It usefully reveals tiktoken exactness, optional model, pay-per-use pricing, and cost (0.001 USDC on Base), but it does not state the output format, behavior without a model, or error/auth requirements. The optional 'modelo' mention also conflicts with the input schema, reducing trust.

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 short, label-segmented, and front-loads the main purpose. Each chunk adds at least some information, though the B2B clause is somewhat cryptic and the 'modelo=optional' note is unreliable.

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 one-parameter tool with no output schema, the description gives the core behavior and cost, but it lacks a clear return-value statement, default-model behavior, and resolution of the missing 'modelo' parameter. It is adequate for a simple count but not fully 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?

The schema only documents 'input' as 'service input'; the description clarifies it is text and mentions an optional 'modelo' parameter. However, 'modelo' is not present in the input schema, so an agent following the description would likely try to pass a parameter the tool may not accept. That misleading extra detail outweighs the minor clarification of 'input' as text.

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 this tool computes an exact token count using tiktoken for a model, which is a specific verb + resource. It does not, however, differentiate itself from the sibling tool 'count-tokens', and the B2B 'budget LLM calls' phrase is vague.

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?

It gives some use-case context by splitting into CONSUMER (exact token count) and B2B (budget LLM calls), implying when to use it. But it offers no exclusions, no comparison to alternatives like count-tokens, and no guidance on when this tool would be inappropriate.

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