SJTU

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Turn your SJTU Zhiyuan No.1 API key into something you can actually use in Claude Code and Codex.

SJTU MCP wraps the SJTU-hosted model API as a local MCP server, so you can call these models directly from your normal agent workflow instead of hand-writing integration scripts over and over again.

Why This Exists

Have you already applied for an SJTU Zhiyuan No.1 API key, but still found it hard to actually use in practice?

This project exists to solve exactly that problem:

• you already have API access
• you want to use it from Claude Code or Codex
• but the SJTU endpoint itself does not plug directly into these agent tools out of the box
• you do not want to rewrite the integration layer every time

Highlights

• Supports Claude Code
• Supports Codex
• Supports both text and vision tasks
• Uses the SJTU OpenAI-compatible endpoint
• Fits naturally into existing MCP workflows

Contents

• Quick Start
• Environment Variables
• Claude Code
• Codex
• Tools
• Example
• Suggested Model Usage
• Notes

Quick Start

For most users, the simplest path is:

1. git clone this repo
2. cd into the project directory
3. install it once
4. add it as a global MCP server in Claude Code or Codex

git clone https://github.com/EternalWavee/sjtu-mcp.git
cd sjtu-mcp
pip install -e .

After installation, your MCP client can start the server automatically when needed. In normal use, you do not need to manually run the server command every time.

Environment Variables

Required:

• SJTU_API_KEY

Optional:

• SJTU_API_BASE_URL
• SJTU_DEFAULT_TEXT_MODEL
• SJTU_DEFAULT_REASONING_MODEL
• SJTU_DEFAULT_VISION_MODEL
• SJTU_REQUEST_TIMEOUT

How to use them:

• .env.example is only a template showing which variables you need
• in actual use, put these values into the env block of your MCP configuration

Claude Code

Recommended: User Scope

Use this if you want sjtu available in all your Claude Code projects on this machine.

claude mcp add sjtu --scope user -- python -m sjtu_mcp.server

Then:

1. open ~/.claude.json
2. find the sjtu entry
3. copy the env section from examples/claude-project.mcp.json
4. replace your-api-key with your real key

Verify:

claude mcp list

Project Scope

Use this if you want to commit a shared config into the repo for teammates.

How to use it:

1. copy examples/claude-project.mcp.json into your project root as .mcp.json
2. replace your-api-key with your real key
3. adjust default models and timeout if needed

Windows / macOS example:

{
  "mcpServers": {
    "sjtu": {
      "command": "python",
      "args": ["-m", "sjtu_mcp.server"],
      "env": {
        "SJTU_API_BASE_URL": "https://models.sjtu.edu.cn/api/v1",
        "SJTU_API_KEY": "your-api-key",
        "SJTU_DEFAULT_TEXT_MODEL": "deepseek-chat",
        "SJTU_DEFAULT_REASONING_MODEL": "deepseek-reasoner",
        "SJTU_DEFAULT_VISION_MODEL": "qwen3vl",
        "SJTU_REQUEST_TIMEOUT": "180"
      }
    }
  }
}

Local Scope

Use this if you only want the server for the current project and do not want to commit the config.

claude mcp add sjtu --scope local -- python -m sjtu_mcp.server

Then add the same env values to the corresponding MCP config entry.

Codex

Recommended: Global Setup

Use this if you want sjtu available in all your Codex projects on this machine.

codex mcp add sjtu -- python -m sjtu_mcp.server

Then:

1. open your own ~/.codex/config.toml
2. copy the content from examples/codex-config.toml
3. replace your-api-key with your real key
4. save and reload Codex or reload MCP

Verify:

codex mcp list

Config File Setup

If you already manage ~/.codex/config.toml directly, you can use this template:

[mcp_servers.sjtu]
command = "python"
args = ["-m", "sjtu_mcp.server"]

[mcp_servers.sjtu.env]
SJTU_API_BASE_URL = "https://models.sjtu.edu.cn/api/v1"
SJTU_API_KEY = "your-api-key"
SJTU_DEFAULT_TEXT_MODEL = "deepseek-chat"
SJTU_DEFAULT_REASONING_MODEL = "deepseek-reasoner"
SJTU_DEFAULT_VISION_MODEL = "qwen3vl"
SJTU_REQUEST_TIMEOUT = "180"

Tools

• sjtu_models
• sjtu_text
• sjtu_vision
• sjtu_cheap_task

Example

Input

请调用 sjtu_vision 分析图片里面的内容 .assets/test.png

Output

Suggested Model Usage

• deepseek-chat
  • default for summaries, rewrites, cleanup, and low-risk text tasks
• minimax or glm-5
  • useful for lightweight rewriting, classification, or extraction
• deepseek-reasoner
  • better for tasks that truly need multi-step reasoning
• qwen3vl
  • a strong starting point for screenshots, OCR-style extraction, and image understanding
• qwen3coder
  • useful for code-adjacent utility tasks

Notes

• This server currently assumes the SJTU endpoint supports OpenAI-compatible /models and /chat/completions.
• Local images are encoded as data URLs before sending.
• If your campus endpoint has model-specific quirks, extend the routing in src/sjtu_mcp/server.py.