Skip to main content
Glama

record_print_dna

Record a 3D print outcome with model fingerprint, settings, and quality grade to enable cross-user learning and print quality analysis.

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.
        

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
Behavior3/5

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

Without annotations, the description explains that the tool saves data for cross-user learning and lists parameters, but it does not disclose side effects, error conditions, or return values. The behavioral details are adequate but incomplete.

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 structured with a clear purpose, prerequisite, and parameter list in docstring format. It is slightly verbose but front-loaded and efficient for the complexity.

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?

Given 14 parameters (11 required) and a nested object, the description covers all inputs with explanations and the prerequisite step. Lacking output schema, it could mention return behavior but is sufficiently complete for a data recording tool.

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 description provides detailed explanations for every parameter, including default values (quality_grade default 'B', print_time_seconds default 0, failure_mode optional), far beyond the schema's bare titles. It fully compensates for the 0% schema description coverage.

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 the tool records a print outcome with full model DNA, distinguishing it from simpler recorders like 'record_print_outcome'. It specifies the purpose of saving model fingerprint, settings, and outcome for cross-user learning.

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?

The description explicitly instructs to use 'fingerprint_model' first to compute fingerprint fields, providing clear prerequisite guidance. However, it does not mention when not to use this tool or suggest simpler alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/codeofaxel/kiln'

If you have feedback or need assistance with the MCP directory API, please join our Discord server