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Kiln

by codeofaxel
OverviewSchemaRelated ServersScoreDiscussions
Python

diagnose_print_failure_live

Diagnose a 3D print failure in real time using live printer state and model geometry to identify root causes and receive ranked actionable fixes.

Instructions

Diagnose a print failure using live printer state + model geometry.

        Unlike ``analyze_print_failure`` (which requires a job_id and
        analyzes historical data), this tool works in real-time by
        reading the current printer state and optionally analyzing
        the model that was being printed.

        Combines printer temperature deltas, bed adhesion analysis,
        overhang geometry, material properties, and printer intelligence
        to produce a ranked diagnosis with actionable fixes.

        Args:
            printer_name: Printer to diagnose.  Omit for the default printer.
            model_path: Path to the model file that was being printed.
                Enables geometry-based diagnosis (adhesion, overhangs).
            material: Filament material (e.g. ``"ABS"``, ``"PLA"``).
            printer_id: Printer model ID (e.g. ``"bambu_a1"``).
                Enables printer-specific intelligence lookup.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
materialNo
model_pathNo
printer_idNo
printer_nameNo
Install Server

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It details that it combines temperature deltas, bed adhesion, overhang geometry, etc., to produce a ranked diagnosis with fixes. This provides good insight into internal behavior.

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 well-structured with a clear main sentence, a distinction paragraph, and a bullet list of parameters. It is slightly lengthy but every sentence adds value, making it effective without being overly verbose.

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?

The tool has no annotations or output schema, but the description covers purpose, parameter semantics, and distinction from sibling. It mentions the output is a 'ranked diagnosis with actionable fixes,' but lacks details on return format. Given the complexity, more specificity would improve completeness.

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 coverage is 0%, but the description's Args section thoroughly explains each parameter: printer_name (omit for default), model_path (enables geometry diagnosis), material (filament type), printer_id (enables intelligence lookup). This adds significant value beyond the 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 clearly states it diagnoses print failures using live printer state and model geometry. It distinguishes itself from analyze_print_failure by specifying real-time vs historical analysis.

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 explicitly contrasts with analyze_print_failure, giving context for when to use this tool. It also describes optional parameters, but does not provide explicit 'when not to use' or alternative sibling tools.

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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