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build_material_overrides

Generate a JSON override dictionary with correct temperatures, speeds, and retraction settings for switching materials on your printer. Combine material database data with printer-specific tuning for ready-to-use reslicing overrides.

Instructions

Auto-generate slicer override dict for a specific material.

Combines material thermal data (from the material database) with
printer-specific tuning (from printer intelligence) to produce a
ready-to-use JSON override dict for ``reslice_with_overrides`` or
``run_reslice_and_print``.

This is the key tool for material switching — call it to get the
correct temperatures, speeds, and retraction settings when changing
from one material to another.

Example workflow::

    # 1. Get overrides for PETG on your printer
    overrides = build_material_overrides("petg", "bambu_a1")
    # 2. Reslice and print with those overrides
    run_reslice_and_print(model_path, overrides=json.dumps(overrides["overrides"]))

Args:
    material_id: Target material (e.g. ``"petg"``, ``"tpu"``).
    printer_id: Optional printer model for printer-specific tuning.
        If omitted, uses material database defaults.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
printer_idNo
material_idYes
Behavior3/5

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

No annotations provided; the description discloses it auto-generates and combines data to produce a JSON dict. It does not mention side effects, error handling, or permissions. The behavior is mostly implied but lacks deep 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, details, and an example. It is relatively concise but the code block adds length; however, it adds value for understanding the workflow.

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 no output schema and moderate complexity, the description explains input, output purpose, and usage in a workflow. It covers the essential context but omits error handling and edge cases.

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?

With zero schema description coverage, the description adds meaning by explaining material_id as the target material and printer_id as an optional printer model. It provides examples and clarifies behavior when omitted, though it doesn't specify allowed values or constraints.

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 auto-generates a slicer override dict for a specific material, combining material thermal data with printer-specific tuning. It distinguishes itself from siblings as the key tool for material switching.

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 provides an explicit example workflow and states it is the key tool for material switching, giving clear when-to-use guidance. However, it does not explicitly mention when not to use or contrast with alternatives like get_material_slicing_profile.

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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