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将您的上海交通大学致远一号 API Key 转化为可以在 Claude Code 和 Codex 中实际使用的工具。
SJTU MCP 将上海交大托管的模型 API 封装为本地 MCP 服务器，因此您可以直接从常规的 Agent 工作流中调用这些模型，而无需反复手写集成脚本。
您是否已经申请了上海交通大学致远一号 API Key，但发现实际上手使用并不方便？
本项目正是为了解决这个问题：
您已经拥有 API 访问权限
您希望在 Claude Code 或 Codex 中使用它
但上海交大的端点无法直接开箱即用地接入这些 Agent 工具
您不想每次都重写集成层
Related MCP server: Claude-LMStudio-Bridge
支持 Claude Code
支持 Codex
同时支持文本和视觉任务
使用上海交大兼容 OpenAI 的端点
自然融入现有的 MCP 工作流
对于大多数用户，最简单的路径是：
git clone 本仓库
cd 进入项目目录
执行一次安装
在 Claude Code 或 Codex 中将其添加为全局 MCP 服务器
git clone https://github.com/EternalWavee/sjtu-mcp.git
cd sjtu-mcp
pip install -e .安装完成后，您的 MCP 客户端可以在需要时自动启动服务器。在正常使用中，您无需每次都手动运行服务器命令。
必需：
SJTU_API_KEY
可选：
SJTU_API_BASE_URL
SJTU_DEFAULT_TEXT_MODEL
SJTU_DEFAULT_REASONING_MODEL
SJTU_DEFAULT_VISION_MODEL
SJTU_REQUEST_TIMEOUT
如何使用：
.env.example 仅作为模板，展示了您需要的变量
在实际使用中，请将这些值放入 MCP 配置的 env 块中
如果您希望在当前机器的所有 Claude Code 项目中使用 sjtu，请使用此方法。
claude mcp add sjtu --scope user -- python -m sjtu_mcp.server然后：
打开 ~/.claude.json
找到 sjtu 条目
从 examples/claude-project.mcp.json 复制 env 部分
将 your-api-key 替换为您真实的 Key
验证：
claude mcp list如果您希望将共享配置提交到仓库中供团队成员使用，请使用此方法。
如何使用：
将 examples/claude-project.mcp.json 复制到项目根目录并命名为 .mcp.json
将 your-api-key 替换为您真实的 Key
根据需要调整默认模型和超时时间
Windows / macOS 示例：
{
"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"
}
}
}
}如果您只想在当前项目中使用该服务器，且不想提交配置，请使用此方法。
claude mcp add sjtu --scope local -- python -m sjtu_mcp.server然后将相同的 env 值添加到相应的 MCP 配置条目中。
如果您希望在当前机器的所有 Codex 项目中使用 sjtu，请使用此方法。
codex mcp add sjtu -- python -m sjtu_mcp.server然后：
打开您的 ~/.codex/config.toml
从 examples/codex-config.toml 复制内容
将 your-api-key 替换为您真实的 Key
保存并重新加载 Codex 或重新加载 MCP
验证：
codex mcp list如果您已经直接管理 ~/.codex/config.toml，可以使用此模板：
[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"sjtu_models
sjtu_text
sjtu_vision
sjtu_cheap_task
请调用 sjtu_vision 分析图片里面的内容 .assets/test.png
deepseek-chat
适用于摘要、重写、清理和低风险文本任务的默认模型
minimax 或 glm-5
适用于轻量级重写、分类或提取任务
deepseek-reasoner
适用于真正需要多步推理的任务
qwen3vl
适用于截图、OCR 风格提取和图像理解的强大起点
qwen3coder
适用于代码相关的实用任务
本服务器目前假设上海交大端点支持兼容 OpenAI 的 /models 和 /chat/completions 接口。
本地图像在发送前会被编码为数据 URL。
如果您的校园端点有特定于模型的怪癖，请在 src/sjtu_mcp/server.py 中扩展路由。
Route common low-risk jobs like summarize, rewrite, classify, and extract.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| content | Yes | ||
| image_path | No | ||
| image_url | No | ||
| model | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only says 'low-risk jobs,' which hints at safety but does not disclose actual behavioral traits like idempotency, side effects, or permission requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise but lacking structure. It is front-loaded with the main purpose, but does not expand on important details, making it minimally adequate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters with no descriptions and no annotations, the description is incomplete. It does not specify valid task types, content format, or how image path/url are used, which is insufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to any of the 5 parameters (task, content, image_path, etc.). It fails to explain valid values or parameter purposes beyond what the schema already shows.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool routes common low-risk jobs like summarize, rewrite, classify, and extract, giving a specific verb and resource. It distinguishes from sibling tools by implying a generic task router, though it could be more precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description only lists example jobs, lacking when-not-to-use or comparisons with siblings like sjtu_text or sjtu_vision.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available models from the SJTU endpoint.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It only states the action (list) but does not explain that it is a read-only operation, any potential side effects, or required permissions. The agent has no additional context beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It conveys the core functionality efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, the description is somewhat adequate but lacks usage context. It does not explain how the output schema relates to usage or provide hints for integration with sibling tools. The presence of an output schema mitigates the need for return value details, but the description could be more helpful by mentioning use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so schema coverage is trivially 100%. The description adds no parameter info, which is acceptable as there is nothing to describe. Baseline 4 for zero parameters is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (List), resource (available models), and source (SJTU endpoint). It effectively differentiates from sibling tools like sjtu_cheap_task, sjtu_text, and sjtu_vision, which target different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or conditions for using sjtu_models.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a plain text task against the SJTU OpenAI-compatible API.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| model | No | ||
| system_prompt | No | ||
| temperature | No | ||
| max_tokens | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as idempotency, side effects, rate limits, or cost. The tool's safety profile (read vs. write) is unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence but lacks necessary detail. It is under-specified rather than appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters, no annotations, and an output schema not described, the description fails to provide a complete picture. The tool's return value and parameter usage are left unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning beyond the parameter names. It does not explain the role of model, system_prompt, temperature, or max_tokens.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the verb 'run' and resource 'plain text task' against a specific API. It distinguishes from vision tasks but does not clarify what 'plain text task' entails compared to the sibling sjtu_cheap_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like sjtu_cheap_task or sjtu_vision. No context on cost, speed, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run an image understanding task against the default vision model.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| image_path | No | ||
| image_url | No | ||
| model | No | ||
| system_prompt | No | ||
| temperature | No | ||
| max_tokens | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral disclosure. It only states that the tool runs an image understanding task, but does not explain side effects, authentication needs, return type, or limitations. The minimal description is insufficient for safe usage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no extraneous content. However, it sacrifices clarity for brevity; it could be more informative without adding much length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters, no annotations, and an existing but undescribed output schema, the description is too minimal. It does not explain parameter interplay (e.g., image_path vs image_url) or output format, leaving significant gaps for effective invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds no explanation for parameters such as prompt, image_path, image_url, model, system_prompt, temperature, or max_tokens, leaving their semantics entirely to interpretation from names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Run an image understanding task against the default vision model', which clearly indicates a verb and resource. However, 'image understanding task' is vague and does not specify the exact capability (e.g., captioning, VQA), and it fails to distinguish from sibling tools like sjtu_cheap_task or sjtu_text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool over alternatives like sjtu_cheap_task or sjtu_text. No prerequisites or exclusions are mentioned, leaving the agent to guess appropriate contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.1sjtu_cheap_tasksjtu_modelssjtu_textsjtu_visionScored across 4 tools
sjtu_cheap_task and sjtu_text both handle text tasks, creating potential confusion. sjtu_cheap_task specifies common low-risk jobs, but the boundary with sjtu_text is unclear. sjtu_models and sjtu_vision are distinct.
All tools share the consistent 'sjtu_' prefix and snake_case naming, but the pattern varies between adjective_noun (sjtu_cheap_task) and noun-only (sjtu_models, sjtu_text, sjtu_vision), which is mostly consistent with minor deviations.
Four tools is well-scoped for the SJTU endpoint, covering essential capabilities (listing models, text, vision, and a cheap task option) without unnecessary bloat.
The set covers core functionalities, but the overlap between sjtu_cheap_task and sjtu_text suggests redundancy; missing streaming or embeddings are minor gaps for basic use.
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