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save_print_checkpoint

Save a checkpoint during a 3D print with current Z height, layer, and temperatures to enable accurate resume from failures on variable-layer-height prints.

Instructions

Save a checkpoint during an active print for accurate resume.

The checkpoint is keyed by ``(printer_name, job_id)`` and read
automatically by :func:`detect_print_failure` so that the resulting
:class:`FailureReport` carries known-good Z / layer / temps.  The
resume planner uses this for accurate ``resume_z_mm`` instead of
estimating from ``z_per_layer * resume_layer`` — meaningfully more
accurate when the print uses variable-layer-height slicing.

Args:
    printer_name: Name of the printer running the job.
    job_id: Unique job identifier.
    z_height: Current Z height in mm.
    layer_number: Current layer number (0-based).
    hotend_temp: Hotend temperature at checkpoint time (Celsius).
    bed_temp: Bed temperature at checkpoint time (Celsius).
    filament_used_mm: Filament consumed so far in mm.
    fan_speed_pct: Part-cooling fan speed (0-100).
    flow_rate_pct: Flow-rate multiplier (default 100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
bed_tempNo
z_heightNo
hotend_tempNo
layer_numberNo
printer_nameYes
fan_speed_pctNo
flow_rate_pctNo
filament_used_mmNo
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the checkpoint's purpose and how it's used by other functions, but does not discuss side effects (e.g., overwriting previous checkpoints), error conditions, or authentication requirements. Adds context but lacks some behavioral details.

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 somewhat long due to the parameter list, but it is well-structured: a clear purpose sentence, followed by explanation, then a parameter block. Could be slightly more concise, but overall efficient and well-organized.

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?

No output schema exists, so the description should explain return values. It does not mention what the tool returns (e.g., success confirmation, checkpoint ID). Given the complexity (9 parameters, no output schema), this is a significant gap.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides a full parameter list with explanations for all 9 parameters, adding meaning beyond the schema (which only has titles and types). This is excellent compensation.

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 clearly states 'Save a checkpoint during an active print for accurate resume', specifies the resource (checkpoint), and explains the keying mechanism and integration with other functions. This distinguishes it from siblings like 'start_print', 'pause_print', etc.

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 description explains the benefit of using this tool (more accurate resume_z_mm) but does not explicitly state when to use it vs alternatives, nor does it provide exclusions or when not to use it. Usage is implied but not clearly guided.

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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