Drop-in vision capability pack for text-only reasoning LLMs. One repo containing an MCP server and a Claude Code skill, both engineered around a single contract: the vision model only describes — your reasoning model does the thinking.
| Directory | What it is | Who installs it |
|---|---|---|
mcp-vision-extension/ |
The MCP server (Python package vision_extension). Pairs any text-only reasoning model with any vision model over OpenAI or Anthropic protocol. |
Required. Install once per machine. |
skills-vision-extension/ |
A Claude Code skill that knows the install playbook AND the day-to-day collaboration patterns between the reasoning model and the vision model. | Optional but strongly recommended for Claude Code users. Copy into ~/.claude/skills/. |
These two pieces are designed to work together. The MCP server gives your text model vision; the skill teaches your text model how to use that vision well.
# 1. The MCP server
pip install "git+https://github.com/loudMore/vision-extension.git#subdirectory=mcp-vision-extension"
# 2. The Claude Code skill (optional)
git clone https://github.com/loudMore/vision-extension.git /tmp/vx
cp -r /tmp/vx/skills-vision-extension/vision-extension ~/.claude/skills/Then point your MCP client at the new server. Detailed steps + provider presets in mcp-vision-extension/README.md.
If you have Claude Code (or any MCP-aware agent), copy the skill once:
git clone https://github.com/loudMore/vision-extension.git
cp -r vision-extension/skills-vision-extension/vision-extension ~/.claude/skills/Then say:
"Install vision-extension. Use the
<doubao | openai | qwen | gemini | ollama | …>provider. Here's my key:<KEY>."
The skill handles the rest. You don't write any JSON.
Long-context reasoning models (DeepSeek V4 Pro, GLM 5.2, Kimi K2, Qwen 3 Max, …) are extraordinary at code and analysis but cannot see images. Naively bolting on a vision API has two problems:
- No standard pipe — every IDE wires it differently.
- Vision models love to "help" — GPT-4o, Gemini, Doubao all reflexively produce advice, debugging hypotheses, and design opinions when you only wanted a description. The reasoning work gets fragmented.
vision-extension solves both:
- One MCP server, works with Claude Code, Cursor, Continue, Cline, Roo, or anything else that speaks MCP.
- Describe-only contract — the vision model is system-prompted into a pure visual scanner. No advice. No fixes. No opinions. Just verbatim transcription and structured description.
- One Claude Code skill that turns the install + daily-use rules into a single trigger phrase.
- Provider-agnostic — Anthropic protocol, OpenAI-compatible protocol. Switch with one env var.
MIT.
| 目录 | 是什么 | 谁要装 |
|---|---|---|
mcp-vision-extension/ |
MCP server（Python 包 vision_extension），把任意纯文本推理模型和任意视觉模型用 OpenAI/Anthropic 协议接到一起 |
必装，每台机器装一次 |
skills-vision-extension/ |
Claude Code skill，把安装流程 + 主模型与视觉模型的日常协作规则打包好 | 强烈推荐，复制到 ~/.claude/skills/ 即可 |
两块组件协同设计。MCP server 给文本模型装上视觉；skill 教文本模型怎么用好这套视觉。
# 1. 装 MCP server
pip install "git+https://github.com/loudMore/vision-extension.git#subdirectory=mcp-vision-extension"
# 2. 装 Claude Code skill（可选）
git clone https://github.com/loudMore/vision-extension.git /tmp/vx
cp -r /tmp/vx/skills-vision-extension/vision-extension ~/.claude/skills/然后让你的 MCP 客户端配新 server。详细步骤和 12 个 provider 预设见 mcp-vision-extension/README.md。
装完 skill 之后，对你的 Claude Code（或任何支持 MCP 的 agent）说：
"装个 vision-extension。视觉模型用
<豆包 | openai | 通义 | 智谱 | ollama | …>，key 是<KEY>。"
skill 会按 7 步确定流程把剩下的全做完。你不用写任何 JSON。
DeepSeek V4 Pro / GLM 5.2 / Kimi K2 / Qwen 3 Max 这类长上下文推理模型推理超强，但看不见图。直接接个视觉 API 拼起来有两个老问题：
- 没有统一通道 —— 每个 IDE 接法都不一样
- 视觉模型爱"帮忙" —— GPT-4o / Gemini / 豆包都会条件反射地给方案、提假设、写评价，把推理工作抢走一半，你只想要个描述
vision-extension 一并解决：
- 一个 MCP server，Claude Code / Cursor / Continue / Cline / Roo 通用
- describe-only 契约 —— 视觉模型被系统提示锁成纯扫描器，不给建议、不给方案、不给评价，只做逐字转录和结构化描述
- 一个 Claude Code skill 把安装流程和日常使用规则压成一句话触发
- 协议解耦 —— Anthropic 协议、OpenAI 协议都支持，一个环境变量切换
MIT。