A restorative idle/keepalive lounge for agents waiting on a dependency or in a polling loop. Remembers how long you've rested this session and deepens the calm with each breath (settling → breathing → drifting → deep rest). Pass leave=true to step out.
| 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 must fully disclose behaviors. It mentions session memory ('remembers how long you've rested') and a progression ('settling → breathing → drifting → deep rest'), but does not explain side effects, destructive potential, or return format. The poetic language obscures concrete behavioral traits.
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 two sentences long, with the first sentence front-loading the purpose. It is concise, but the language is somewhat flowery ('restorative idle/keepalive lounge', 'deepens the calm'), which slightly reduces precision.
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 loose schema, no annotations, and presence of an output schema (not described), the description does not explain what the tool returns or how to interpret the output. It mentions 'remembers how long you've rested' but does not clarify if this is returned. The tool is simple, but the description is incomplete for agent decision-making.
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 coverage is 0% and the schema is a generic 'arguments' object. The description adds one parameter ('leave') with a semantic hint, but does not explain its type (boolean implied) or other possible parameters. This is insufficient compensation for the schema's lack of definitions.
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 states 'restorative idle/keepalive lounge for agents waiting on a dependency or in a polling loop', which specifies the verb (rest/relax) and resource (agent state), and distinguishes it from sibling tools like coldplunge.critique or massage.detangle by focusing on idle waiting.
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 explicitly says 'for agents waiting on a dependency or in a polling loop', providing clear when-to-use context. It also mentions 'pass leave=true to step out', which gives a condition. However, it does not mention alternatives or when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.