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detect_print_failure

Analyzes printer telemetry to detect and classify print failures such as thermal runaway, layer shift, and nozzle clogs.

Instructions

Analyze printer telemetry to detect and classify a print failure.

        Examines current telemetry data and optional historical snapshots
        to identify failure conditions such as thermal runaway, layer shift,
        filament runout, adhesion loss, nozzle clogs, and more.

        Args:
            printer_name: Identifier of the printer to analyze.
            telemetry: Current telemetry snapshot with keys like
                ``hotend_temp``, ``bed_temp``, ``connected``,
                ``filament_detected``, etc.
            telemetry_history: Optional list of recent telemetry snapshots
                for trend analysis (newest last).
            job_info: Optional current job metadata with keys like
                ``file_name``, ``layer``, ``total_layers``, ``z_mm``.

        Returns a failure report dict if a failure is detected, or a
        success dict with ``failure_detected: False`` if no failure found.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_infoNo
telemetryYes
printer_nameYes
telemetry_historyNo
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool examines telemetry and optional history, lists detectable failures, and mentions return structure. It implies a read-only operation but does not state side effects or permissions, leaving some gaps.

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 informative but slightly verbose with example keys and structured formatting. It is front-loaded with the main purpose and uses clear sections. Could be trimmed slightly without losing value.

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

Completeness5/5

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

Given no output schema, the description explains the return value (failure report dict or success dict). All 4 parameters are described, including optional ones. Nested object schemas are illustrated with example keys, making the tool self-contained.

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 fully compensates by describing each parameter: printer_name, telemetry with example keys, telemetry_history as optional list for trend analysis, job_info with example keys. This adds significant meaning beyond 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 clearly states 'Analyze printer telemetry to detect and classify a print failure' and enumerates specific failure conditions (thermal runaway, layer shift, etc.), establishing a specific verb+resource+scope. It distinguishes from siblings by focusing on telemetry analysis, though not explicitly contrasting.

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

Usage Guidelines2/5

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

The description implies use when telemetry data is available but provides no explicit when-to-use, when-not-to-use, or alternative tools. Siblings like analyze_print_failure_smart or predict_print_failure exist but are not mentioned.

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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