Sentiment

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

The description discloses that the tool costs credits from the account wallet, which is valuable behavioral context beyond the annotations. The annotations already cover safety and idempotence, and nothing in the description contradicts 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?

Two sentences with no filler. The first sentence front-loads the purpose and outputs; the second adds an important operational cost warning. 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?

For a simple two-parameter tool with no output schema, the description covers what the tool does, what inputs it expects, what outputs it produces, and a key operational constraint. It could add the score's scale or format, but the description is substantially complete for selection and invocation.

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 only 50%: the required 'text' parameter has no schema description, and the description helps by narrowing it to reviews or support messages. The optional 'aspects' parameter is documented in the schema and echoed by 'per-aspect breakdown,' but no length limits, examples, or formatting guidance are provided.

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 uses a specific verb ('Turn ... into') and names concrete outputs: a sentiment score, per-aspect breakdown, and driving themes. It clearly identifies the input type (review or support message), and these outputs distinguish it from sibling tools like parserail_classify or parserail_summarize.

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 gives clear usage context: use this for sentiment analysis of reviews or support messages. It does not explicitly name alternatives or exclusion cases, so it misses the top bar, but the intended use is unambiguous.

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