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Kiln

by codeofaxel
OverviewSchemaRelated ServersScoreDiscussions
Python

get_print_diagnostic

Diagnose print quality issues by combining troubleshooting data, printer compatibility, storage requirements, and post-processing tips into one actionable response.

Instructions

Get a comprehensive print diagnostic combining multiple knowledge sources.

        This is the PRIMARY tool for debugging print problems.  Combines
        troubleshooting data (symptom matching, root causes, fixes),
        printer compatibility (upgrade requirements, known issues),
        storage requirements (drying temps, humidity limits), and
        post-processing tips (strengthening options) into a single
        actionable response.

        Call this FIRST when a user reports any print quality problem.
        It cross-references all knowledge sources so the agent doesn't
        need to make multiple tool calls.

        Examples:
            material="petg", symptom="stringing", printer="ender3"
            material="abs", symptom="warping", printer="bambu_a1"
            material="nylon", symptom="brittle"

        Args:
            material: Material being printed (e.g. "pla", "petg").
            symptom: What's going wrong (e.g. "stringing", "warping",
                "poor adhesion", "clog", "brittle").
            printer: Optional printer model for compatibility context
                (e.g. "ender3", "bambu_x1c").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
printerNo
symptomNo
materialYes

Tool Definition Quality

A3.9/5.0
Behavior2/5

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

With no annotations provided, the description bears full burden for behavioral disclosure. It describes the tool's functionality but omits any behavioral traits such as side effects, authentication requirements, or rate limits.

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 paragraphs and bullet points, front-loading the purpose. While it is somewhat lengthy, each section adds value and the examples are helpful.

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 the tool's complexity (3 parameters, one required) and lack of output schema or nested objects, the description covers the diagnostic scope, usage examples, and knowledge sources comprehensively. It provides sufficient context for an agent to invoke the tool correctly.

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 schema has 0% description coverage for parameters, but the description adds detailed explanations and examples for material, symptom, and printer, significantly enhancing meaning beyond the schema's minimal definitions.

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 explicitly states the tool's purpose as getting a comprehensive print diagnostic combining multiple knowledge sources. It differentiates from sibling tools by stating it's the PRIMARY tool for debugging print problems and cross-references all knowledge sources, avoiding multiple tool calls.

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 clearly instructs to call this FIRST when a user reports a print quality problem, providing strong usage context. However, it does not explicitly mention when not to use it or alternative 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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