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

Annotations already declare readOnlyHint=true, so the safe-read profile is covered. The description adds useful context that the data is AI-generated and tied to the Aircall AI Voice feature, plus the underlying REST endpoint, but it does not disclose return format, pagination, auth requirements, or rate limits.

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?

Two short sentences with the core purpose front-loaded. The REST endpoint detail is useful but slightly redundant with the tool name and endpoint path already implied; overall it is efficient and easy to scan.

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 simple one-parameter read tool with annotations covering safety, the description tells the agent what it retrieves and from which endpoint. It does not describe the returned sentiment structure, but the complexity is low enough that the definition remains usable without an output schema.

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 single 'id' parameter is fully documented in the schema itself. The description adds no parameter semantics beyond what the schema provides, which matches the baseline 3 for high-coverage schemas.

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?

States a specific verb and resource: 'Get the AI-generated sentiment analysis of a call.' This clearly distinguishes it from sibling call-detail tools like get_call_summary, get_call_topics, and get_call_transcription by naming the distinct resource (sentiments), though it does not explicitly name or contrast 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 the use case (retrieving sentiment analysis for a specific call) but provides no explicit when-to-use guidance, no exclusions, and no routing to alternatives such as get_call_summary or get_call_transcription.

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