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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context by listing per-endpoint pricing and regulatory grounding, but it does not explicitly state that the tool is read-only, what happens on errors, or any side effects. The pricing transparency is valuable, but other behavioral aspects like data handling or rate limits remain undisclosed.

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 summary, coverage note, and a bulleted endpoint list, and it is front-loaded with the core purpose. However, it is quite long and repeats endpoint names that also appear in the schema's enum, making it less concise than ideal for an AI agent to quickly 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?

The tool has 20 parameters and no output schema, yet the description does not specify which parameters are needed per endpoint, nor does it explain return formats or error behavior. An agent cannot reliably invoke a specific action without guessing which parameters apply, making the description incomplete for a tool of this complexity.

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 baseline is 3. The long description adds context about endpoints but does not explain which parameters are required for each action. Some parameter descriptions in the schema are minimally informative (e.g., 'code', 'lang'), though a few like 'bottles' and 'fragrances' have meaningful explanations. Overall, the description contributes little beyond the schema's parameter names.

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 identifies the tool's domain ('Fragrance intelligence') and enumerates specific capabilities (note profiles, batch-code decoding, dupe matching, etc.), which distinguishes it from sibling 'pulse' tools focused on other domains. However, it lacks a single explicit verb like 'search' or 'lookup,' instead relying on a list of endpoint names to convey actions.

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 when to use the tool through its domain coverage (global, EU regs, Middle East attar market, Asia) and the self-descriptive endpoint names. It does not provide explicit 'when not to use' guidance or mention alternatives, so the usage context is only inferred rather than clearly defined.

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