analyze_sentiment

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

Without annotations, the description must disclose behavior. It notes that 'include_url' defaults to False to save tokens and mentions title deduplication for 'limit'. However, it does not explicitly state that the tool is read-only, nor does it cover authentication, rate limits, or potential side effects.

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 with a brief summary, usage suggestion, parameter list, return description, and example. It is concise without unnecessary redundancy, using bullets and clear formatting.

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 the tool's moderate complexity and the presence of an output schema, the description covers essential aspects including parameter details and output structure. It lacks error handling or edge cases, but is otherwise complete for an AI agent.

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?

With 0% schema description coverage, all six parameters are explained in the description, including defaults and constraints (e.g., 'limit' max 100, 'date_range' format). The explanations are clear, though possible platform values are not enumerated.

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 specifies the tool's function: analyzing sentiment tendency and heat trends of news. It uses a specific verb and resource ('analyze sentiment and heat trends'), but does not explicitly differentiate from sibling tools like 'analyze_topic_trend' that may perform similar analyses.

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 a suggestion to use 'resolve_date_range' for natural language dates and includes an example. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'search_news' for raw news retrieval) or conditions where it might not be appropriate.

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