analyze_content_sentiment

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

With no annotations, the description carries the burden of behavioral disclosure. It states the return format but does not explicitly confirm that the tool is read-only, what data source is used, or any limitations. For a simple analysis tool, the implicit read-only nature may suffice, but there is minimal behavioral context beyond the output.

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?

A single sentence that front-loads the core purpose and output; there is zero waste.

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?

The tool has only 2 parameters, 1 required, and no output schema. The description adequately conveys the purpose and output format at a high level, though it could elaborate on the exact structure of the distribution or the nature of 'content' (e.g., search results, web pages). Overall, it is a complete-enough description for an agent to invoke the tool correctly on a basic task.

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 schema already fully documents both parameters. The tool description adds no additional semantic context beyond what the schema provides, hence baseline 3.

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 uses a specific verb 'Analyze' with resource 'content related to a keyword' and names the output ('positive/negative/neutral distribution and connotations'), clearly distinguishing it from the myriad of other keyword/research tools in the sibling list.

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 implies usage when sentiment analysis is needed, but does not explicitly state when to use this vs alternatives or exclude other cases. Since no sibling tool directly overlaps, context is clear.

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