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

Beyond the readOnly and openWorld annotations, the description discloses pricing (10 credits per successful call, 1 credit = 1 CAD cent), billing exclusion for schema-invalid input, the neutral threshold (below 0.6 confidence), and output field meanings. These are material behavioral traits an agent needs before invoking a paid operation.

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 dense but front-loaded with the core purpose (labels, score range, classifier). Every clause adds useful information about output shape, threshold, or billing, though the single long sentence could be split for readability.

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?

With no output schema, the description responsibly explains the return object fields (label, score, positive, negative, chunks) and the neutral threshold. It also covers billing behavior and redaction, making it complete for this single-parameter paid tool.

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%, and the schema already documents the 1–8,000 character limit and PII redaction. The description largely repeats the length limit and adds no new syntax or formatting guidance, so baseline 3 is appropriate.

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 names a specific capability: it runs a sentiment classifier over text and returns positive/negative/neutral with a -1 to 1 score. This is clear, but it does not explicitly distinguish itself from sibling tools like classify, entity-extract, or summarize that an agent might also consider for text analysis.

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 explicit guidance on when to choose sentiment over siblings such as classify or entity-extract, nor any exclusions or prerequisites. The agent must infer appropriate use from the purpose alone.

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