compute_ageCompute coffee age in days off roast and resting/staling verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| bean_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| bean_id | Yes | ||
| verdict | Yes | ||
| age_days | Yes | ||
| warnings | Yes | ||
| roast_date | Yes | ||
| rest_window | Yes | ||
| days_off_roast | Yes | ||
| grams_remaining | Yes | ||
| days_since_opened | Yes | ||
| effective_age_days | Yes | ||
| frozen_days_excluded | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds output semantics (days off roast and verdict) but does not disclose details such as how the age is derived or prerequisites like the bean needing a roast date.
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, front-loaded sentence with no wasted words. It efficiently communicates the operation and its result in a compact, scannable form.
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 simple one-parameter schema, supportive annotations, and the existence of an output schema, the description is complete. There is no missing information an agent would need to correctly invoke this tool in normal use.
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 0% and the description does not explicitly mention bean_id. However, the single parameter's name is already self-explanatory, and 'coffee age' clearly links the bean_id to the coffee being evaluated, providing enough implied semantic context for a one-parameter tool.
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 uses a specific verb, 'Compute', names the resource, 'coffee age', and states the exact outputs: days off roast and a resting/staling verdict. This clearly differentiates it from all sibling tools, none of which compute age.
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: whenever coffee age in days off roast or resting/staling verdict is needed. However, it does not explicitly state alternatives or exclusions, though no sibling tool competes with this functionality.
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 target a clearly distinct resource and action, and the list/register/update/set tool families are easy to tell apart. The closest pair is diagnose_preview and diagnose_shot, which are well-described but similar enough in name that an agent could select the wrong one.
The overwhelming majority of tools follow a consistent verb_noun pattern (list_beans, register_grinder, update_shot, set_active). Minor exceptions like grinder_math and kb_changelog lack the imperative verb prefix, but they are readable and do not create real confusion.
34 tools is above the 25+ threshold and feels heavy even though the domain is fairly rich. The many parallel list_* and register_* tools for beans, grinders, machines, scales, waters, programs, and recipes could plausibly be consolidated or trimmed without losing core capability.
The core shot lifecycle is well covered: log, update, delete, diagnose, and list shots, plus bean registration and maintenance tracking. However, most registered entities lack update/delete tools, and get_rule has no corresponding list_rules tool, leaving some obvious workflow gaps that agents must work around.