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create_isosurface_3d

Generate a 3D isosurface from scattered volumetric data to highlight regions where a scalar value lies within a specified threshold. Use for pollution concentration, isotherms, or groundwater surfaces.

Instructions

Interactive 3D iso-surface from a volumetric scalar field (WebGL, orbit-able).

Renders the 3D boundary where value_column falls within [isomin, isomax] — a level set of a continuous fourth variable sampled across (x, y, z). The marching-cubes extraction runs client-side at render, so scattered samples are accepted without a regular grid or SciPy.

Ideal for: pollution/concentration thresholds across a 3D monitoring volume, isotherms, groundwater head surfaces, any "region where value ≥ threshold".

Returns: {filepath, title, rows}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesRow dicts (one per volumetric sample point)
themeNo'dark', 'light', or 'professional'dark
titleNoChart title
isomaxNoUpper scalar bound (defaults to column max)
isominNoLower scalar bound (defaults to column min)
opacityNoSurface opacity (0–1)
filenameNoOutput filename (without .html)isosurface_3d
x_columnYesColumn for the X axis
y_columnYesColumn for the Y axis
z_columnYesColumn for the Z axis (depth)
colorscaleNoPlotly colorscale name for value mappingViridis
value_columnYesColumn whose level sets define the surface

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries full behavioral burden. It clearly discloses client-side rendering, WebGL interaction, orbit capability, and that scattered samples are accepted without a regular grid. It also states the return object format. While it doesn't cover rate limits or permissions, for a visualization tool these are sufficient.

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

Conciseness5/5

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

The description is two paragraphs long, with the first paragraph explaining the core function and technical detail, and the second providing ideal use cases. Every sentence adds value, and the key purpose is front-loaded. No unnecessary words.

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 has 12 parameters and no annotations, the description is fairly complete, covering the algorithm, use cases, and output format. However, it could mention limitations (e.g., performance with large datasets) or what 'rows' in the return value refers to. Overall, it provides sufficient context for an agent to understand the tool's purpose and behavior.

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?

Schema coverage is 100%, so the description does not need to add much per-parameter detail. The description adds context about how parameters like isomin/isomax define the level set and that the marching cubes algorithm runs client-side, but this is supplementary. A baseline of 3 is appropriate given complete schema coverage.

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 creates an interactive 3D iso-surface from a volumetric scalar field. It specifies the specific verb and resource, and distinguishes from sibling tools by mentioning client-side marching cubes and acceptance of scattered samples without a regular grid.

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 explicitly lists ideal use cases (pollution thresholds, isotherms, etc.) and mentions scattered samples are accepted. However, it does not explicitly state when not to use this tool or provide direct comparisons to sibling tools like create_surface_3d or create_volume_3d.

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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