analyze_sentiment

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does this well by revealing meaningful behaviors: default date range is today, limit defaults to 50 and max 100, titles are deduplicated, platforms default to all, sorting defaults by heat weight, and include_url defaults to false to save tokens. It does not mention error cases or data-freshness limitations, but for a read-style analysis tool this is adequate.

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 well structured and front-loaded: purpose, routing advice, parameter list, return summary, and example. It contains no filler or redundant explanation, and every section serves a practical purpose. The format makes it easy for an agent to scan and extract the needed information.

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 six parameters, no annotations, and an output schema, the description is largely complete: all parameters are documented, return contents are summarized, an example is provided, and sibling routing is mentioned. The output schema exists, so detailed return fields do not need to be enumerated. The main missing element is clearer guidance on when to choose this tool over sibling analysis tools, and the available platform IDs are not listed.

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 fully document the parameters, and it does. Every argument is explained with concrete details: date_range gets an explicit JSON format, platforms gets a realistic example, limit gets a maximum and deduplication note, and boolean flags get their default behavior. This fully compensates for the schema's lack of descriptive 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 the tool's function: analyze news sentiment and heat trends (分析新闻的情感倾向和热度趋势). It names a specific action and resource, and the parameter list clarifies that it works on a topic/platform/date range. However, it does not explicitly distinguish itself from similar sibling tools like analyze_topic_trend or analyze_data_insights, so it falls just short of a 5.

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 provides an explicit routing suggestion: if using natural-language dates, call resolve_date_range first. This is direct, actionable guidance for a specific alternative. It does not, however, discuss when this tool should be preferred over the other analysis-oriented siblings, so the usage guidance is clear but not exhaustive.

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