compare_languages

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

With no annotations, the description carries full burden. It details the return values (english/chinese aggregates and divergence) and explains the behavioral trait that large divergence often precedes mood shifts. It does not mention destructive behavior (none needed) or auth/rate limits, but is transparent about output and interpretation.

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 three sentences, front-loaded with purpose. It is clear but slightly verbose in explaining divergence meaning and examples. Every sentence adds value, but could be tightened.

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?

Given no output schema, the description fully explains return values and divergence formula. It covers purpose, usage context, parameters, and behavioral insights. For a 2-parameter tool with 100% schema coverage, it leaves no gaps.

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%, so baseline is 3. The description adds value for ticker by listing useful examples (Taiwan-listed, US semis, ADRs), but for window_hours it largely repeats schema info (default, max, min). Overall, schema already documents parameters well.

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 explicitly states the tool compares English vs Traditional Chinese news sentiment side-by-side with a divergence score, and specifies its purpose for cross-market arbitrage signal detection. It is distinct from siblings like get_sentiment (single language) and get_sentiment_batch (batch for one language).

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 explains when to use (for cross-market arbitrage) and provides context about US press leading Taiwan/Asia press, as well as interpreting divergence. It lacks explicit 'when not to use' or comparison with sibling tools, but the context is clear.

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