dcc-mcp-blender: Instructions file for Gemini CLI

GEMINI.md

dcc-mcp-blender GEMINI.md is an instructions file for Gemini CLI from dcc-mcp/dcc-mcp-blender. It costs 582 tokens per session, scanned A, original, MIT.

A guide for connecting Google Gemini or Vertex AI to a Blender server that exposes Blender actions over the web. It also shows how to create Blender scripts and read structured results.

In plain words
What is it for?
Use it to generate Blender skill scripts, call more than 200 Blender tools through an MCP-compatible client, and process success or error results returned as JSON.
Why use it?
It explains how to use Gemini's code-writing and structured-data strengths with Blender without guessing how the integration works.

Instructions file for Gemini CLI

Written for Gemini CLI: the file is GEMINI.md. Also seen: mentions AGENTS.md; mentions Gemini CLI.

This is dcc-mcp/dcc-mcp-blender's own configuration. It tells Gemini CLI how to work on dcc-mcp-blender itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything dcc-mcp-blender configures →

Reuse

Borrowing it

Nothing to install: this file belongs to dcc-mcp/dcc-mcp-blender. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/dcc-mcp/dcc-mcp-blender/main/GEMINI.md
Clone the repo
git clone --depth 1 https://github.com/dcc-mcp/dcc-mcp-blender

Made for: Gemini CLI.

Wrote this? Show the measurements

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README.md
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Per session 582 This file is loaded in full into every session.
When invoked 582 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00582 $0.00582
Opus 5 $0.00291 $0.00291
Sonnet 5 $0.00116 $0.00116
Haiku 4.5 $0.00058 $0.00058

Measured 9d ago against content hash dd889a4b6da4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

dcc-mcp-blender GEMINI.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

GEMINI.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GEMINI.md — Google Gemini / Vertex AI Integration Guide

Gemini-specific integration notes for dcc-mcp-blender. For the full project map, see AGENTS.md.


What This Project Does

dcc-mcp-blender embeds an MCP Streamable HTTP server directly inside Blender. Gemini (via an MCP-compatible client or custom integration) can discover and invoke 200+ Blender tools over HTTP.


Gemini-Specific Strengths

Gemini excels at code generation and structured output parsing. Leverage these when working with dcc-mcp-blender:

1. Skill Script Generation

Ask Gemini to generate new Blender skill scripts using the dcc_mcp_blender.api helpers:

from dcc_mcp_blender.api import blender_success, blender_error

def batch_rename(prefix: str) -> dict:
    """Rename selected objects with prefix."""
    import bpy
    selected = bpy.context.selected_objects or []
    renamed = []
    for obj in selected:
        obj.name = f"{prefix}{obj.name}"
        renamed.append(obj.name)
    return blender_success("Renamed objects", renamed=renamed, count=len(renamed))

2. Structured Tool Results

Gemini handles nested JSON well. Parse blender_success / blender_error results directly:

{
  "success": true,
  "message": "Created sphere",
  "context": {
    "object_name": "Sphere",
    "radius": 1.0
  }
}

3. Skill Search & Discovery

Use Gemini's search capability with the built-in discovery tools:

  • search_skills("render batch") → returns matching skills with descriptions
  • search_tools(query="bake", tags=["texture"]) → filtered search

Integration Setup

If your Gemini client supports MCP over HTTP, configure:

Gateway endpoint: http://127.0.0.1:9765/mcp
Protocol: MCP Streamable HTTP (2025-03-26 spec)

Gemini-Specific Tips

  • Code-first workflows: Gemini can write complete skill packages. Generate SKILL.md, tools.yaml, and scripts/*.py in one shot, then place them in a directory listed in DCC_MCP_BLENDER_SKILL_PATHS.
  • Image understanding: Feed capture_viewport base64 PNGs back to Gemini for visual state verification.

Read the full file on GitHub · 79 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 79 lines · 582 tokens per session scan A dd889a4b6da4

Subscribe to this mod's changes

dcc-mcp-blender GEMINI.md is an instructions file published in the GitHub repository dcc-mcp/dcc-mcp-blender (29 stars, last pushed yesterday), licensed MIT. It adds 582 tokens to every session, about $0.0029 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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