ShotPulled

Log maintenance

log_maintenance

Log a cleaning or maintenance job against a piece of equipment. A task that takes the thing apart opens a calibration epoch, and every recipe locked before it is read through that epoch's offset — so record the offset here if you counted the clicks while reassembling, which is the one moment anybody actually knows it. Read the available tasks, their intervals and their due state from list_grinders.

Input Schema

TableJSON Schema

Name	Required	Description
`note`	No	Optional — what you did, and anything that surprised you
`task`	Yes	Which maintenance job was performed
`details`	No	Fields specific to this task. Which ones a task takes is published as details_fields in the task catalog on list_grinders; a burr clean takes none
`evidence`	No	One line recording what the measurement rested on, e.g. "Counted 6 clicks coarser at reassembly."
`equipment_id`	Yes	The ID of that piece of equipment
`offset_value`	No	The correction, if you measured it now: clicks to ADD to a pre-clean reading to get the same grind today, signed along the collar's own number direction. Omit it and the epoch stays unmeasured, which is a state the app shows rather than a zero it assumes
`performed_at`	No	When it was done (ISO 8601 or YYYY-MM-DD HH:MM:SS, UTC). Defaults to now; backdating is fine
`disturbs_zero`	No	Whether this job disturbed the reference the stored numbers are read against — for a burr clean, whether the burrs actually came out. Defaults to the task's own answer; send 0 for a wipe-down that left the collar alone
`offset_source`	No	How the offset was arrived at: pull (a reference shot), counted (clicks counted at reassembly), typed (asserted directly)
`equipment_kind`	Yes	Which kind of equipment this job was done on

Output Schema

TableJSON Schema

Name	Required	Description	Default
`entry`	Yes
`message`	Yes
`entry_id`	Yes

Schema Changelog

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

AddedAug 17, 2026

TDQS

A4.3/5.0

Behavior5/5

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

It discloses the non-obvious calibration-epoch side effect: taking the thing apart opens an epoch, and locked recipes are read through that epoch's offset. It also explains why the offset should be captured at reassembly, which is exactly the kind of behavioral context annotations (all false) cannot supply.

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?

Three dense sentences, each carrying distinct information: purpose, epoch/offset behavior, and the authoritative source for task data. No filler or repetition of the title.

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

Completeness4/5

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

For a 10-parameter tool with an output schema and enums, the description covers the non-obvious domain behavior and points to the dynamic task catalog. It does not mention what the response contains or how to choose between this and set_maintenance_offset, but the output schema and parameter docs close the former gap.

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

Parameters4/5

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

The schema already documents every parameter in detail (100% coverage), so the description's baseline is 3. It earns an extra point by explaining the offset_value semantics in domain terms — 'if you counted the clicks while reassembling, which is the one moment anybody actually knows it' — and by pointing to list_grinders for task-specific details_fields.

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 first sentence uses a direct verb and object — 'Log a cleaning or maintenance job against a piece of equipment' — so the action is unambiguous. It does not, however, explicitly contrast with sibling tools such as set_maintenance_offset, which covers part of the same domain and could be invoked instead.

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

Usage Guidelines4/5

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

It tells the agent where to discover valid tasks and due states ('Read the available tasks, their intervals and their due state from list_grinders') and gives a concrete trigger for including the offset. It stops short of naming when-not-to-use this tool or comparing it to set_maintenance_offset.

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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