This record as markdown: /tools/holdmybeer-gg-blend-ai/render-image.md
What render_image does on Blend Ai
AI agents invoke render_image to trigger actions in Blend Ai. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why render_image is rated High
Rendering an image in Blender triggers an external computational operation (invoking Blender's render engine), which consumes significant CPU/GPU resources and writes output files to disk. This qualifies as Execute due to triggering an external operation with effects depending on the current scene state. The description is empty, which lowers confidence.
From the tool's definition Tool name 'render_image' on a Blender MCP server with 108 tools for 3D modeling, animation, and rendering.
Attacks that exploit this kind of access
The rule that runs render_image safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Blend Ai, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For render_image, this is the rule to start with:
render_image stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Blend Ai, apply this rule, and every render_image call is checked against it from then on.
Questions about render_image
render_image is a execute tool on the Blend Ai MCP server. It is categorised as a Execute tool in the Blend Ai MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Blend Ai MCP server in PolicyLayer and add a rule for render_image: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Blend Ai. Nothing to install.
render_image is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the render_image rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for render_image. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
render_image is provided by the Blend Ai MCP server (holdmybeer-gg/blend-ai). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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