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render_from_camera

Render a still image from a specified camera in Blender, with configurable resolution and sample count to produce high-quality previews.

Instructions

Render a still from the specified (or active) camera.

Parameters:

  • camera_name: Camera to render from (uses scene active camera if omitted)

  • width: Render width in pixels (default 1920)

  • height: Render height in pixels (default 1080)

  • samples: Sample count for Cycles and EEVEE (default 32)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
widthNo
heightNo
samplesNo
camera_nameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.5

TDQS

A3.5/5.0
Behavior2/5

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

There are no annotations, so the description must carry the full behavioral disclosure burden. It does reveal that camera_name is optional and falls back to the active camera, and that samples applies to Cycles/EEVEE, but it omits critical behavior: whether the render is returned as an image, saved to disk, or yields no output value. It also doesn'ttate whether this is a blocking operation or whether it mutates Blender scene state.

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 compact and front-loaded: a one-sentence purpose statement followed by a clean parameter list. Every line carries useful information, and there is no fluff or redundant prose. It is likely about as short as it could be while still conveying the necessary parameter behavior.

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?

For a render tool with no output schema and no annotations, the description does not specify what the caller receives after rendering: an image path, a base64 payload, a success flag, or nothing. It also doesn't mention whether Blender must already be running or whether the active camera must exist. The parameter list is solid, but the operational contract of the tool is incomplete.

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 description coverage is 0%, so the description must compensate, and it does thoroughly. It explains all four parameters with meaningful semantics: camera_name's active-camera fallback, the pixel dimensions, and that samples controls both Cycles and EEVEE. These details go well beyond the raw schema types and defaults.

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 opens with a specific verb and resource: 'Render a still from the specified (or active) camera.' This clearly identifies the tool's singular purpose and distinguishes it from siblings like render_all_cameras and render_depth_map. The scope is unambiguous and immediately actionable.

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?

No explicit guidance is given about when to use this tool versus alternatives such as render_all_cameras, render_depth_map, or get_viewport_srcreenshot. The description doesn't mention when-not-to-use scenarios or prerequisites like needing a camera in the scene or Blender running. The use case must be inferred entirely from the tool name and the terse statement.

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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