MiniMax MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MiniMax MCP Server search the web for today's technology headlines"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MiniMax MCP Server
A fully functional MiniMax Token Plan MCP Server, providing four capabilities: quota query, web search, image understanding, and image generation. Built on Python FastMCP, it can be directly integrated into MCP-supported clients like Claude Code.
Feature Overview
Tool | Function | MiniMax API Used |
| Query remaining quota for Token Plan models |
|
| MiniMax web search |
|
| Image understanding (dual mode + cache) |
|
| Image generation (image-01 model) |
|
| Clear vision analysis cache | - |
Related MCP server: minimax-mcp
Project Structure
minimax-mcp/
├── config.env.example # 配置模板
├── pyproject.toml
├── src/minimax_mcp/
│ ├── server.py # FastMCP 服务入口 + 5 个 Tool
│ ├── client.py # MiniMax HTTP API 客户端
│ ├── config.py # 配置管理(config.env → 环境变量 → 默认值)
│ ├── tools/
│ │ ├── quota.py # 额度查询(5小时/日周期分类)
│ │ ├── web_search.py # 网页搜索
│ │ ├── image_understand.py # 图片理解(本地文件→Base64 转换)
│ │ └── image_generate.py # 图片生成(自动保存本地)
│ └── vision/
│ ├── analyzer.py # 分析器(双模式 + 缓存调度)
│ ├── prompts.py # 结构化 Prompt 模板
│ └── cache.py # LRU 内存缓存 + 磁盘 JSON 持久化
└── tests/Quick Start
1. Get API Key
Obtain your API Key from the MiniMax Platform Token Plan page.
2. Installation
# 克隆仓库
git clone <repo-url>
cd minimax-mcp
# 安装依赖(需要 Python >= 3.10)
pip install -e .
# 或使用 uv
uv pip install -e .3. Configuration
cp config.env.example config.env
# 编辑 config.env,填入你的 API Keyconfig.env structure:
# 必需
MINIMAX_API_KEY=你的_api_key
# 可选
MINIMAX_API_HOST=https://api.minimaxi.com # 中国大陆
# MINIMAX_API_HOST=https://api.minimax.io # 全球
MINIMAX_IMAGE_OUTPUT_DIR=~/Pictures/MiniMax # 生成图片保存位置
MINIMAX_CACHE_DIR=~/.minimax-mcp/cache # 视觉分析缓存位置
MINIMAX_VISION_DEFAULT_MODE=detailed # quick 或 detailed
MINIMAX_CACHE_MAX_SIZE=256 # 最大缓存条目
MINIMAX_CACHE_TTL_DAYS=7 # 缓存过期天数Important:
config.envcontains the API Key and is excluded in.gitignore. Do not commit to Git.
4. Register to Claude Code
Add to your Claude Code MCP configuration:
{
"mcpServers": {
"MiniMaxMCP": {
"command": "uv",
"args": ["run", "--directory", "<项目路径>/minimax-mcp", "minimax-mcp"]
}
}
}Or use the system Python directly:
{
"mcpServers": {
"MiniMaxMCP": {
"command": "python",
"args": ["-X", "utf8", "-m", "minimax_mcp.server"],
"env": {
"PYTHONPATH": "<项目路径>/minimax-mcp/src"
}
}
}
}Tool Details
1. query_quota — Quota Query
Query the model quota usage of the Token Plan, automatically distinguishing between text models (5-hour cycle) and other models (daily cycle).
query_quota()
→ {
text_models: [
{ model_name: "MiniMax-M3.5", used: 32, total: 100, remaining: 68, usage_pct: 32 }
],
other_models: [
{ model_name: "image-01", used: 5, total: 50, remaining: 45, usage_pct: 10 }
],
no_quota: [...],
summary: "文本模型: 5:00:00 后重置 | 其他模型: 12:30:00 后重置"
}2. web_search — Web Search
Search for web content via the MiniMax search engine.
web_search(query="OpenAI GPT-5 发布日期")
→ {
success: true,
results: [
{ title: "...", url: "...", snippet: "...", position: 1 },
...
],
related_searches: [...],
query: "OpenAI GPT-5 发布日期"
}3. understand_image — Image Understanding
Drawing on the design philosophy of OpenHanako Vision Bridge, it provides two analysis modes.
Design Philosophy:
Offload images to a specialized vision model (MiniMax VLM) for structured analysis
Inject analysis results as text into the LLM context, allowing pure text models to "understand" image content
Built-in LRU cache (256 entries, disk-persistent); identical image + identical prompt does not consume extra quota
Dual Modes:
Mode | Use Case | Output Format |
| Quickly understand image content | ~300-word concise description |
| In-depth analysis | 7-dimension structured report |
detailed mode output dimensions:
Dimension | Content |
| Image overview |
| Visible text |
| Objects and layout |
| Charts/data |
| Answer to user question |
| Analysis evidence |
| Uncertainty notes |
understand_image(
image_url="https://example.com/photo.jpg", # 支持 HTTP URL 或本地路径
prompt="图片里有什么错误提示?", # 可选,特定问题
mode="detailed", # quick 或 detailed
use_cache=true # 默认启用缓存
)Note: MiniMax VLM does not support coordinate output, so OpenHanako's Visual Primitives spatial coordinate annotation capability is not yet implemented. If coordinate-aware image analysis is required, it is recommended to use a model that supports visual primitives.
4. generate_image — Image Generation
Generate images using the MiniMax image-01 model.
generate_image(
prompt="A serene lake at sunset with snow-capped mountains",
model="image-01", # 目前仅支持 image-01
aspect_ratio="16:9", # 1:1 / 16:9 / 9:16 / 3:4 / 4:3
n=1, # 1-3 张
prompt_optimizer=true, # 启用提示词自动优化
save_to_disk=true, # 自动保存到本地
response_format="base64" # base64(可存本地)或 url(24h临时链接)
)Images are automatically saved to the MINIMAX_IMAGE_OUTPUT_DIR directory (default ~/Pictures/MiniMax).
Architecture Design
┌─────────────────┐ MCP Protocol ┌──────────────────────┐
│ Claude Code / │ ◄──────────────────► │ FastMCP Server │
│ MCP Client │ (stdio) │ (server.py) │
└─────────────────┘ │ │
│ ┌─────────────────┐ │
│ │ quota.py │ │
│ │ web_search.py │ │
│ │ image_*.py │ │
│ │ vision/analyzer │ │
│ │ vision/cache │ │
│ └────────┬────────┘ │
│ │ │
│ ┌────────▼────────┐ │
│ │ MiniMaxClient │ │
│ │ (HTTP/HTTPS) │ │
│ └────────┬────────┘ │
└───────────┼───────────┘
│
┌───────────▼───────────┐
│ MiniMax API │
│ api.minimaxi.com │
└───────────────────────┘Configuration Priority
config.env 文件 → 环境变量 → 代码默认值Caching Mechanism (Vision Analysis)
Memory Cache: LRU strategy, max 256 entries (configurable)
Disk Persistence: JSON format, stored in
MINIMAX_CACHE_DIRCache Key:
SHA256(image_url + prompt + mode)TTL: 7-day expiration by default
Dependencies
Python >= 3.10
mcp >= 1.0.0 (FastMCP / MCP Protocol)
httpx >= 0.27.0 (HTTP client)
Pillow >= 10.0.0 (Image processing)
Development
# 克隆并安装开发依赖
git clone <repo-url>
cd minimax-mcp
pip install -e ".[dev]"
# 运行测试
python -m pytest tests/
# 直接启动 MCP 服务器
python -m minimax_mcp.serverReferences
OpenHanako Vision Bridge — Architecture reference for the image understanding module
License
MIT
Available Tools
5 toolsclear_vision_cacheA
清除图片理解缓存(手动触发)
将内存中的缓存数据强制写入磁盘,并返回当前缓存统计。
Inputs: no named parameters.
generate_imageA
使用 MiniMax image-01 生成图片,默认保存到本地
图片自动保存到配置的输出目录(默认 ~/Pictures/MiniMax)。
Args: prompt: 图片描述文本(英文效果最佳,中文也可) model: 模型名称,默认 image-01 aspect_ratio: 图片比例:1:1 / 16:9 / 9:16 / 3:4 / 4:3,默认 1:1 response_format: 默认 base64(自动存本地),改 url 则仅返回24h临时链接 n: 生成数量 1-3,默认 1 prompt_optimizer: 是否启用提示词优化,默认 true save_to_disk: 是否保存到本地(仅 base64 模式有效),默认 true
Inputs: prompt (required), model, aspect_ratio, response_format, +3 more.
query_quotaA
查询 MiniMax Token Plan 剩余额度
返回文本模型(5小时周期)和其他模型(日周期)的配额使用情况, 包括已用次数、剩余次数、使用百分比、重置时间等。
无需参数,使用 config.env 中配置的 API Key。
Inputs: no named parameters.
understand_imageA
分析图片内容 — 借鉴 OpenHanako Vision Bridge 设计
两种模式:
quick: 简洁描述(~300词),适合快速了解图片内容
detailed: 结构化 7 维度分析(image_overview / visible_text / objects_and_layout / charts_or_data / answer_to_request / evidence / uncertainty),参考 OpenHanako vision-bridge.js
支持缓存:同一图片+相同prompt不重复调用API(LRU + 磁盘持久化)
Args: image_url: 图片 URL(HTTP/HTTPS)或本地文件路径,支持 JPEG/PNG/GIF/WebP (≤20MB) prompt: 对图片的具体问题,如 "这张图片里有什么错误提示?" mode: "quick" 或 "detailed",默认 detailed use_cache: 是否使用缓存,默认 true
Inputs: image_url (required), prompt, mode, use_cache.
web_searchB
使用 MiniMax 进行网络搜索
Args: query: 搜索查询词,例如 "OpenAI GPT-5 发布日期"
Inputs: query (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 5 tools
Each tool targets a distinct operation (cache clearing, image generation, quota query, image analysis, web search) with no overlap in purpose.
All tool names follow a consistent verb_noun snake_case pattern (e.g., clear_vision_cache, generate_image, query_quota, understand_image, web_search).
Five tools is a well-scoped set for a server offering image generation, analysis, web search, cache management, and quota checking—each tool serves a clear and necessary function.
The tool set covers the core functionalities of the MiniMax AI service: image generation, image understanding, web search, cache management, and quota monitoring, with no obvious gaps.
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