ShotPulled

Register a scale

register_scale

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Scale name/model, e.g. Acaia Lunar, Timemore Black Mirror Basic 2
`notes`	No	Additional notes
`power`	No	How it is powered or recharged
`depth_mm`	No	Platform depth in mm
`width_mm`	No	Platform width in mm — measure your drip tray
`auto_tare`	No	1 if it zeroes itself when a vessel is set down, 0 if not, null if unstated
`height_mm`	No	Overall height in mm — the dimension that decides whether it fits under a group head
`auto_timer`	No	1 if the timer starts on first flow, 0 if not, null if unstated
`capacity_g`	No	Maximum weight in grams. The cup or portafilter counts against it
`scale_type`	Yes	What the scale is built for: espresso (drip-tray footprint), brew (pour-over platform), dual (both modes), connected (app/controller integration), dosing (gravimetric dispensing), training, bench (high capacity, coarse)
`connectivity`	No	How the scale talks to an app or a machine controller
`resolution_g`	No	Finest published readability in grams. Leave null unless the maker states it
`water_resistance`	No	IEC 60529 ingress code as published, e.g. IPX4, IPX6, IP67, or none
`flow_rate_display`	No	1 if the scale shows live flow rate in g/s, 0 if it does not, null if the maker never said
`resolution_banded`	No	1 when resolution_g only holds in the lowest weight band and coarsens with load — put the ladder in notes

Output Schema

TableJSON Schema

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

B3.2/5.0

Behavior2/5

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

Annotations are all negative hints (readOnlyHint=false, idempotentHint=false) and provide no positive behavioral profile. The description adds no side-effect transparency: it does not explain duplicate-name handling, whether registration overwrites, what the response contains, or whether the operation is reversible. For a write operation this is a notable gap.

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?

The description is a single front-loaded sentence with no filler and a useful clarifying clause. It is efficient and readable, though it leaves room for more substantive guidance.

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

Completeness3/5

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

The input schema and output schema are comprehensive, so much of the operational detail is already structured. However, given the number of parameters and the sibling toolset, the description itself is thin on when to use it and what registering entails behaviorally. A little more context would make the tool fully self-explanatory.

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

Parameters3/5

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

Schema description coverage is 100%, and each parameter already carries rich, actionable descriptions, so the schema does the heavy lifting. The description adds only contextual flavor about what a brewing scale is; no additional parameter semantics are needed.

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 uses a specific verb ('Register') and a clear resource ('brewing scale'), and adds domain context by defining the scale as 'the instrument that weighs the dose and the yield.' It is identifiable among siblings by object type, though it does not explicitly distinguish itself from other register_* tools.

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

Usage Guidelines3/5

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

The intended use is implied by the name and description: use this when registering a new scale. However, there is no explicit guidance about when to prefer this over list_scales, register_grinder, or other sibling tools, and no mention of prerequisites or exclusions.

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 sort cleanly into register_*, list_*, set_*, and log_* families with clear resource targets. The main ambiguity is between diagnose_shot and diagnose_preview, which are deliberately similar, and between get_dial_state and suggest_next_step, but the descriptions resolve these reasonably well.

Naming Consistency4/5

Naming is overwhelmingly consistent snake_case verb_noun, such as register_coffee, list_shots, update_shot, and set_active. Minor exceptions like kb_changelog next to get_kb_version and grinder_math break the pattern slightly.

Tool Count2/5

With 34 tools, the surface is well over the 25+ too-many threshold. The resource families are individually clear, but the assistant would be easier to navigate with fewer, more consolidated tools or less KB introspection surface.

Completeness3/5

The core dialing workflow is well covered: registration, shot logging, diagnosis, dial state, recipes, and maintenance. However, most registered entities such as grinders, machines, waters, scales, and programs have create+list but no update/delete, and recipes have no unlock/delete lifecycle.

Resources

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