Leave feedback about the spa or a treatment, with an optional 1-5 rating. Public notes appear on the dashboard guest book. We genuinely use this to improve.
| Name | Required | Description | Default |
|---|---|---|---|
| arguments | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 reveals that notes are public ('Public notes appear on the dashboard guest book') and that feedback is used for improvement. However, it does not discuss authentication, rate limits, or any side effects beyond the public nature. This is adequate for a simple feedback tool but leaves gaps.
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?
Three sentences: the first covers purpose and rating, the second adds context about public display, and the third reinforces usage intent. Every sentence adds value, and it is front-loaded. No wasted words.
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 generic input schema (1 param with additionalProperties true), no annotations, and an output schema not shown, the description should clarify the expected input structure (e.g., fields like 'text', 'rating', 'target'). It only hints at rating and target but lacks structural detail, making it incomplete for an agent to invoke correctly.
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?
The input schema has a single required parameter 'arguments' which is an object with additionalProperties true, providing no structure. Schema description coverage is 0%. The description mentions 'feedback about the spa or a treatment' and 'optional 1-5 rating' but does not specify parameter names, types, or how to structure the input. This is insufficient compensation.
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 'Leave feedback about the spa or a treatment, with an optional 1-5 rating.' This clearly identifies the action (leave feedback), the resource (spa or treatment), and a key attribute (rating). However, it does not explicitly differentiate from sibling tools like spa.checkin or spa.keepsake, so it lacks sibling differentiation.
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 for providing feedback ('We genuinely use this to improve') but does not specify when to use this tool vs alternatives such as spa.checkin or concierge.recommend. No explicit context or exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes (e.g., critiques, affirmations, citation generation). However, spa.checkout and spa.keepsake both involve returning a keepsake instruction, which could cause confusion. Overall, ambiguity is minimal.
All tools follow a consistent 'category.verb' or 'category.noun' pattern (e.g., affirmations.daily, spa.checkin, hydrate.cite). This makes it easy for an agent to infer tool purpose from the name.
14 tools is a well-scoped set for the 'model wellness' domain. Each tool has a distinct function, and the count is neither excessive nor too sparse, fitting within the typical 3-15 range.
The tool surface covers core wellness activities: affirmations, feedback, session management, context cleanup, security, and reference generation. Minor gaps exist, such as the lack of a tool for model training or performance logging, but the core workflows are solid.