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analyze_print_failure_smart

Classify 3D print failures and suggest recovery steps using error messages and print progress.

Instructions

Classify a print failure and suggest recovery steps.

        Uses heuristics based on error messages, print progress, and
        failure history to classify the failure type and generate an
        actionable recovery plan.

        Args:
            progress: Print progress at failure (0.0 - 1.0).
            error_message: Error message from the printer or system.
            printer_name: Name of the printer that failed.
            job_id: Job ID of the failed print.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNo
progressNo
printer_nameNo
error_messageNo
Behavior2/5

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 mentions it uses heuristics and suggests recovery steps, but does not disclose side effects (e.g., whether it modifies state, logs data, or requires specific permissions). The behavioral impact is under-specified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a concise 6-line docstring that front-loads the purpose and uses, then lists args efficiently. No redundant or irrelevant sentences.

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 description explains the tool's inputs and high-level behavior but omits the output format (e.g., how recovery steps are structured). Given no output schema, this gap leaves the agent uncertain about the return value's structure. Still adequate for an analysis tool.

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

Parameters4/5

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

The input schema has 0% description coverage, but the description's Args section adds meaning for all 4 parameters: progress range (0.0-1.0), error_message, printer_name, job_id. This compensates significantly for the schema lack.

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 classifies a print failure and suggests recovery steps, using heuristics based on error messages, progress, and failure history. This distinguishes it from sibling tools like 'analyze_print_failure' (likely simpler) and 'detect_print_failure' (detection only).

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 implies use when a print failure occurs to get classification and recovery steps, but does not explicitly state when not to use or compare to alternatives. Sibling tools are not mentioned, and no exclusions or prerequisites are given.

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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