ShotPulled

Register an espresso machine

register_machine

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Machine name/model, e.g. Dedica EC685
`notes`	No
`photo`	No	Photo path or URL
`tools`	No	JSON or comma-separated tools, e.g., paper filters, flow control, metal mesh
`archived`	No	1 if archived, 0 if active
`prep_type`	No	Preparation equipment type. One of: espresso_machine, lever_press, moka_pot, aeropress, french_press, v60_dripper, cold_brew. Lower snake_case.
`prep_style`	No	Preparation style. One of: espresso, filter, immersion, percolation. Only "espresso" is diagnosed by the dialing engine (G8) — anything else is recorded but never dialed, so do not guess this field.
`boiler_type`	No
`control_type`	Yes
`connected_device`	No	Metadata mapping to smart hardware APIs
`basket_size_grams`	No
`built_in_grinder_id`	No	id of a grinder built into this machine (bean-to-cup / integrated grinder)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`message`	Yes
`machine_id`	Yes

TDQS

C2.7/5.0

Behavior2/5

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

Annotations are all false and offer no safety profile. The description implies creation but does not disclose whether duplicates are allowed, what validation occurs, or what the response looks like. With no annotation coverage, the description carries the burden and fails to provide meaningful behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no wasted words, front-loaded with the verb and resource. However, its brevity means it forgoes the chance to include any behavioral or usage detail, so it is concise but thin.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 12-parameter tool with a subtle prep_style constraint and a family of register_* siblings, this description is too thin. It doesn't explain the 'alternative preparation machine' scope, the required control_type, or how it relates to update or list tools. An output schema exists, so return values are covered, but call-level context is largely missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 67%, leaving notes, boiler_type, control_type, and basket_size_grams undocumented in both schema and description. The description adds no parameter-level meaning and does not help disambiguate closely related fields like prep_type vs. prep_style.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (Register) and resource (espresso or alternative preparation machine), which clearly identifies the tool's core purpose. It distinguishes from sibling register_* tools by resource type (machine vs. grinder/scale/coffee), though it doesn't explicitly name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like register_grinder or list_machines. The only implied usage is the word 'register' meaning create a new machine, but no exclusions or prerequisites are stated.

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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TDQS

B3.4/5.0

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness3/5

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.

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