get_sentiment_insights

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool is 'Powered by AI classification,' hinting at automated analysis, but doesn't cover critical aspects like whether it's read-only, requires permissions, has rate limits, or what the output format entails. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 highly concise and front-loaded, consisting of a single sentence that directly states the tool's function and an additional clarifying phrase. Every word earns its place, with no redundant information, making it efficient and easy to parse.

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 lack of annotations and output schema, the description is incomplete for a tool that performs sentiment analysis. It doesn't explain the return values (e.g., breakdown format), error conditions, or behavioral constraints like data freshness or access requirements. For a tool with no structured output information, this leaves the agent under-informed.

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 100%, so the input schema fully documents the two parameters ('period' and 'app_id'). The description adds marginal value by implying analysis across 'app, category, and time period,' which aligns with parameters but doesn't provide additional syntax or format details beyond the schema. This meets the baseline for high schema coverage.

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 purpose: 'Get sentiment analysis breakdown (positive/neutral/negative) by app, category, and time period.' It specifies the verb ('Get'), resource ('sentiment analysis breakdown'), and dimensions of analysis. However, it doesn't explicitly differentiate from sibling tools like 'get_conversation' or 'list_conversations' that might also involve sentiment-related data, keeping it from a perfect score.

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 no guidance on when to use this tool versus alternatives. It mentions 'by app, category, and time period' but doesn't specify prerequisites, exclusions, or compare to siblings like 'get_conversation' for detailed sentiment. Without explicit usage context, the agent lacks direction on tool selection.

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