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

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 already indicate this is a non-read-only, non-idempotent write, but the description adds no behavioral detail beyond the word "Register." It does not mention record creation, duplicate behavior, required ownership, or any side effects, so transparency beyond annotations is minimal.

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

Conciseness5/5

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

The description is a single clear sentence with no filler. It is front-loaded with the core purpose and does not waste tokens on redundant information.

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?

Given a 12-parameter registration tool with an output schema, the description is thin, but the input schema covers most parameters with useful descriptions such as prep_style and prep_type. Missing context includes when to register a machine versus other equipment and the behavioral implications of creating a non-idempotent record, so the description is only partially complete.

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%, and the description itself adds no parameter-level meaning. The phrase "espresso or alternative preparation machine" loosely hints at prep_type, but the schema already documents that. Important fields like control_type and boiler_type remain undocumented in the description, and the tool description does not compensate for the uncovered parameters.

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

Purpose5/5

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

The description uses a specific verb and resource: "Register an espresso or alternative preparation machine." It clearly identifies the object being created and distinguishes this tool from siblings like register_grinder and register_program by naming the machine resource directly.

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?

There is no guidance on when to use this tool versus alternative registration tools, nor any mention of prerequisites or exclusions. The only signal is the word "machine" in the name and description, leaving the agent to infer applicability.

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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