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mcp-servers-for-revit

MCP server for Revit - Python

color_splash

Color Revit elements in a category based on parameter values, assigning identical colors to matching parameter values.

Instructions

Color elements in a category based on parameter values

This tool applies color coding to Revit elements within a specified category based on their parameter values. Elements with the same parameter value will receive the same color.

Args: category_name: Name of the category to color (e.g., "Walls", "Doors", "Windows") parameter_name: Name of the parameter to use for coloring (e.g., "Mark", "Type Name") use_gradient: Whether to use gradient coloring instead of distinct colors (default: False) custom_colors: Optional list of custom colors in hex format (e.g., ["#FF0000", "#00FF00"]) ctx: MCP context for logging

Returns: Results of the coloring operation including statistics and color assignments

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
category_nameYes
parameter_nameYes
use_gradientNo
custom_colorsNo
Behavior3/5

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

With no annotations, the description must fully disclose behavior. It explains that elements with the same parameter value get the same color, and mentions gradient and custom colors. However, it does not disclose whether colors overwrite existing graphics, whether it applies to view-specific or element-level overrides, or if it requires an active Revit document.

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 concise with two clear paragraphs and an Args/Returns list. It is front-loaded with the core action. However, the Args section is somewhat redundant with the schema, and the Returns statement is present but no output schema is defined.

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?

Given 4 parameters, no output schema, and no annotations, the description adequately covers the operation and parameter roles. However, it lacks details on prerequisites (e.g., active document, element selection), error handling, performance implications, and whether the coloring is persistent or view-specific.

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?

Schema coverage is 0%, so the description must compensate. It explains each parameter with examples (e.g., 'Walls' for category_name, 'Mark' for parameter_name) and describes the effect of use_gradient and custom_colors. This adds significant meaning beyond the schema's bare types.

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's purpose: 'Color elements in a category based on parameter values' with a specific verb ('color') and resource ('elements in a category'). It distinguishes itself from the sibling 'clear_colors' tool, which reverses the operation.

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?

The description implies usage for color-coding based on parameters but does not explicitly state when to use it versus alternatives like 'clear_colors' or other modeling tools. No prerequisites or exclusions are provided, leaving the agent to infer context.

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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