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pzfreo

build123d-mcp

render_view

Render 3D solids or 2D shapes with auto-detected pipeline. Supports PNG, SVG, DXF outputs, camera presets, clipping, and object labeling for visual verification.

Instructions

Render model. Auto-detects 3D vs 2D: solids use the VTK tessellation path; 2D shapes (Sketches, edge Compounds, dimensioned drawings) use the ezdxf+matplotlib raster path — review dimensioned drawings the same way as 3D parts. Renders confirm appearance, not geometry — verify booleans with measure() first. format: 'png' (raster, default), 'svg' (HLR line drawing, works without a display), 'dxf' (HLR projection as parseable polylines for downstream 2D CAD), or 'both' (PNG + SVG together). If the PNG path fails (headless host), falls back to SVG automatically. direction: top, front, side, iso. azimuth/elevation: camera rotation in degrees applied after the direction preset. objects: comma-separated names or name:color pairs e.g. 'u_frame:blue,roller:red' (default: all, auto-coloured). quality: standard, high. clip_plane: x, y, z to slice; clip_at: absolute world coordinate along that axis (default: each mesh's midpoint). save_to: optional file path; for format='both' writes .png and .svg. mode: 'auto' (default; no solids + flat in Z = 2D), or '2d'/'3d' to force a pipeline when auto-detection picks wrong (e.g. a Compound mixing a Sketch and a solid routes to 3D); the path used is reported as 'Rendered via pipeline.' colors: optional dict mapping object names and special layer keys (_dims, _labels) to colour names or '#aabbcc'; overrides name:color syntax and the default dimension colour (2D PNG/SVG only; ignored for 3D and DXF). label_objects: when true, each named object is labelled at its centroid in the PNG. highlights: optional list of entities to label, e.g. [{"object": "bracket", "type": "edge", "index": 5, "label": "hinge_edge"}]; type is 'face', 'edge', or 'vertex', index matches shape.faces()/edges()/vertices(); the object must be registered with show() and in the rendered set. Labels are PNG-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
directionNoiso
objectsNo
qualityNostandard
clip_planeNo
clip_atNo
azimuthNo
elevationNo
save_toNo
formatNopng
label_objectsNo
highlightsNo
colorsNo
modeNoauto
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: auto-detection logic, fallback behavior, mode forcing, rendering paths, and limitations (appearance vs geometry). No hidden behaviors.

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

Conciseness3/5

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

Very long single paragraph; while informative, it lacks structure (bullet points or sections). Every sentence adds value, but readability suffers.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (13 parameters, no output schema), the description is exceptionally complete, covering all parameters, behavior, and edge cases. No gaps identified.

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

Parameters5/5

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

Schema coverage is 0%, but the description explains every parameter's purpose, syntax, and defaults with examples (e.g., objects format, highlights array). Adds significant value beyond the raw schema.

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?

The description clearly states 'Render model' and explains auto-detection between 2D and 3D, differentiating from potential sibling tools like render_drawing by mentioning dimensioned drawings but not explicitly contrasting. The purpose is specific and actionable.

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?

Provides extensive guidance on formats, fallbacks, parameter options, and caveats like verifying booleans with measure(). However, lacks explicit comparison to sibling tools such as render_drawing or inspect_drawing.

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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