analyze_sentiment

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

With no annotations, the description carries the full behavioral disclosure burden. It adds useful details like title deduplication, default sorting by weight, and include_url defaulting to false to save tokens, plus the return structure.

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 front-loaded with the purpose, then organized into Args, Returns, and Examples. Every line adds value, and the example clarifies real usage without bloat.

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 6-parameter tool with no schema descriptions and no provided annotations, the description covers inputs, defaults, output shape, and a related helper tool. It is slightly incomplete only in not routing the agent away from similar analysis siblings.

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 description coverage is 0%, so the description must compensate—and it does. Every parameter is explained with format, defaults, examples, or constraints, such as date_range format, limit maximum, and deduplication behavior.

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 action and resource: analyzing news sentiment and popularity trends. It is clear but does not explicitly differentiate itself from similar analysis siblings like analyze_topic_trend or analyze_data_insights.

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 practical usage context, including the recommendation to call resolve_date_range for natural-language dates and the default behavior for platforms and date ranges. It does not, however, state when to prefer this tool over related analysis tools.

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