blender-mcp

preview_views

Render multi-angle EEVEE previews from a .blend file to evaluate a scene's appearance. Quickly generates front, three-quarter, side, and top views as PNGs for visual critique.

Instructions

Render multi-angle EEVEE previews (front, three_quarter, side, top) for critique.

Args: blend_file: .blend to open and preview. output_dir: Folder under output/ for the PNGs. width: Preview width. height: Preview height. samples: Low sample count for speed. engine: Prefer EEVEE. target: Usually headless.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "blend_file": "<blend_file>"
    },
    "name": "preview_views"
  }
}

Input Schema

Name	Required	Default
`width`	No
`engine`	No	EEVEE
`height`	No
`target`	No	headless
`samples`	No
`blend_file`	Yes
`output_dir`	No	previews

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 18, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations, the description must fully disclose behavior. It mentions low sample count and EEVEE engine preference but does not address critical traits like file modification, output handling, error behavior, or whether it opens a viewer. This leaves significant gaps for safe usage.

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 short and to the point, with a clear two-sentence summary followed by a parameter list. It avoids fluff, though the parameter list could be more structured (e.g., types or defaults) without losing conciseness.

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

Completeness2/5

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

Given the absence of annotations, output schema, and usage guidelines, the description fails to provide enough context for an AI to use this tool effectively. It does not explain the return value, interaction with sibling tools, or prerequisites, leaving the agent underinformed.

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?

The description includes a parameter list with brief explanations for all 7 parameters, compensating for the 0% schema coverage. However, explanations are minimal (e.g., 'Preview width' adds little beyond the name), and details like valid engine values or the exact meaning of 'target' are omitted.

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 renders multi-angle EEVEE previews (front, three_quarter, side, top) for critique. This specifies both the verb (render) and the resource (specific angles), distinguishing it from sibling tools like render or viewport_screenshot which may produce single or different output.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus others like render, render_and_view, or view_preview. It lacks explicit when-to-use, when-not-to-use, or alternative recommendations, leaving the agent to infer from context alone.

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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