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welingtonmatos

Bambu Studio MCP

compare_slices

Read-onlyIdempotent

Slice one model with multiple settings or values and compare print time, filament grams, colour changes, support time and flat-top deviation. Nothing is saved.

Instructions

Slice the same model several ways and compare time, grams, colour changes, support time and how far flat tops fall from the layer grid. vary='process' (values optional: the sensible layer heights for the nozzle) or any setting key with explicit values, e.g. vary='support_type', values=['tree(auto)','normal(auto)']. Nothing is saved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
varyYes
nozzleNo
valuesNo
objectsYes
printerYes
processNo
settingsNo
filamentsNo
auto_adjustNo
object_typeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent and non-destructive; the description usefully adds 'Nothing is saved,' which confirms no side effects. It names the metrics that get compared, but gives no detail on run cost, error modes, or how results are surfaced.

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?

Front-loads the purpose, then drops into parameter guidance with a concrete example; three sentences with little waste. Dense but every clause carries information.

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?

For a 10-parameter, nested-object tool with no output schema, the description usefully enumerates the comparison outputs (standing in for a return spec) and explains the pivotal vary parameter. Yet most parameters remain undocumented in both schema and description, so an agent cannot fully configure it from this text alone.

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

Parameters3/5

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

Top-level schema coverage is 0%, so the description must carry parameter meaning; it does clarify vary and values with concrete examples ('support_type', ['tree(auto)','normal(auto)']). However the other eight parameters (nozzle, printer, process, settings, filaments, auto_adjust, object_type, objects) are unexplained, leaving a substantial gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Slice the same model several ways and compare') plus the exact comparison dimensions (time, grams, colour changes, support time, flat-top deviation). This distinguishes it in spirit from slice_model/slice_project, but it never names or contrasts those siblings explicitly.

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?

Provides strong how-to guidance for the vary parameter ('process' vs any setting key, with examples), but never says when to pick this tool over slice_model or slice_project, nor any exclusions. Usage is implied by 'compare' rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.