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Kiln

by codeofaxel
OverviewSchemaRelated ServersScoreDiscussions
Python

get_print_diagnostic

Identify root causes and fixes for 3D printing issues by analyzing material, symptom, and optional printer model. Cross-reference troubleshooting, compatibility, storage, and post-processing data to deliver a single action plan.

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
symptomYes
materialYes
Install Server

TDQS

A4.3/5.0
Behavior3/5

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 implies a read-only diagnostic operation by describing combination of data sources, but does not explicitly state it is non-destructive or discuss permissions or side effects. The context added (cross-referencing, actionable response) is helpful but incomplete for full transparency.

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 summary, primary usage, list of combined data, and examples. It is informative but could be slightly more concise (e.g., the list of data sources could be shortened). Still, every sentence adds value.

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 (combining multiple diagnostic sources) and lack of output schema, the description adequately explains what the tool returns: a single actionable response with troubleshooting, compatibility, storage, and post-processing info. However, it does not detail the response structure, which would be helpful for an AI agent.

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, so the description compensates well by explaining each parameter: material (e.g. 'pla'), symptom (e.g. 'stringing'), printer (optional, for compatibility context). Examples illustrate valid values, adding significant meaning beyond the schema's bare type info.

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 'Get a comprehensive print diagnostic' and emphasizes it is the primary tool for debugging print problems, combining multiple knowledge sources. It distinguishes itself from siblings like troubleshoot_print_issue by claiming a broader scope and cross-referencing.

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

Usage Guidelines5/5

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

Explicitly instructs 'Call this FIRST when a user reports any print quality problem.' and explains it eliminates the need for multiple tool calls. Provides concrete examples showing typical usage patterns.

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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