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record_print_dna

Save a print outcome with full model DNA, including geometric signatures and settings, to enable cross-user learning and distinguish designs that share older signatures.

Instructions

Record a print outcome with full model DNA.

        Saves the model fingerprint alongside print settings and outcome
        for cross-user learning.  Use ``fingerprint_model`` first to
        compute the fingerprint fields.

        Args:
            file_hash: SHA-256 hash of the model file.
            geometric_signature: Geometric signature from fingerprinting.
            triangle_count: Number of triangles in the model.
            surface_area_mm2: Total surface area in mm^2.
            volume_mm3: Model volume in mm^3.
            overhang_ratio: Ratio of overhanging triangles (0.0-1.0).
            complexity_score: Model complexity (0.0-1.0).
            printer_model: Printer model name.
            material: Material used (e.g. ``"PLA"``).
            settings: Print settings dict.
            outcome: ``"success"``, ``"failed"``, or ``"partial"``.
            quality_grade: Grade from ``"A"`` to ``"F"`` (default ``"B"``).
            failure_mode: Optional failure description.
            print_time_seconds: Print duration in seconds.
            geometric_signature_v2: ``fingerprint_model``'s
                ``geometric_signature_v2``.  Pass it: it is what lets
                this print be told apart from a different design that
                happens to share the older signature.  Omitted, the row
                is stored with the older key only and can never be
                separated from that design later.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
outcomeYes
materialYes
settingsYes
file_hashYes
volume_mm3Yes
failure_modeNo
printer_modelYes
quality_gradeNoB
overhang_ratioYes
triangle_countYes
complexity_scoreYes
surface_area_mm2Yes
print_time_secondsNo
geometric_signatureYes
geometric_signature_v2No
Install Server

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It discloses the persistence side effect ('Saves the model fingerprint...'), explains the cross-user learning purpose, and highlights a significant consequence: omitting `geometric_signature_v2` permanently stores the row with the older key and prevents later separation from another design. It stops short of describing the return value or error behavior, but the core behavioral impact is covered.

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 long, but the tool has 15 parameters so a detailed Args block is justified. The one-sentence summary and prerequisite are front-loaded, and each parameter line adds semantic value. The `geometric_signature_v2` paragraph is somewhat lengthy but necessary because it explains a non-obvious data-integrity consequence.

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 15-parameter tool with no output schema and no annotations, this description is unusually complete: it defines every input, names the prerequisite tool, explains the persistence side effect, and calls out the v2-signature pitfall. The main gaps are the absence of a described return value and no detail about the expected structure inside the `settings` dict.

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?

The schema has zero property descriptions and no enums, so the description is the only source of parameter semantics. It explains units (`surface_area_mm2`, `volume_mm3`), ranges (`overhang_ratio`, `complexity_score`), valid values (`success`, `failed`, `partial`, grades `A`-`F`), defaults, and the especially important meaning of `geometric_signature_v2`. This fully compensates for the bare schema.

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 opens with a specific verb and resource: 'Record a print outcome with full model DNA.' It then clarifies the distinguishing feature—saving the model fingerprint for cross-user learning—which separates it from simpler sibling tools like `record_print_outcome` or `record_recovery_check`.

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 gives an explicit prerequisite: 'Use fingerprint_model first to compute the fingerprint fields.' This tells the agent when to use the tool in a workflow. It does not state when not to use it or explicitly name alternatives, but the fingerprint-first instruction provides clear operational guidance.

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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