customerthermometer_get_happiness_value

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 does not state whether this is a read-only operation, how the percentage is calculated, what happens when filters are applied, whether results are paginated, or what the response format looks like. The description adds minimal behavioral context beyond the name.

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 a single concise sentence that front-loads the core purpose. It is efficient and free of filler, though it could add a brief note about filtering behavior without becoming verbose.

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 tool with no annotations, no output schema, and four optional parameters, the description is thin. It does not explain the return value structure, how the Happiness Factor is computed, or how filters affect the result. An agent would need to infer too much to call this tool correctly in varied scenarios.

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 documents all four parameters. The description adds the context that these parameters filter the response set, but it does not explain how the parameters interact (e.g., whether fromDate/toDate are required together, or how blastID relates to the date range). Baseline 3 is appropriate since the schema does the heavy lifting.

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 verb ('Get') and resource ('Happiness Factor as a percentage'), and mentions the account or a filtered set of responses. It is clear enough to distinguish from siblings like get_nps_value and get_comments, though it does not explicitly name those alternatives.

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 by mentioning 'account or a filtered set of responses', and the parameters (fromDate, toDate, blastID, limit) suggest filtering. However, it does not explicitly state when to use this tool versus alternatives like get_nps_value or get_blast_results, nor does it provide exclusions or prerequisites.

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