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Verwandeln Sie Ihren SJTU Zhiyuan No.1 API-Schlüssel in etwas, das Sie tatsächlich in Claude Code und Codex verwenden können.
SJTU MCP kapselt die von der SJTU gehostete Modell-API als lokalen MCP-Server, sodass Sie diese Modelle direkt aus Ihrem normalen Agenten-Workflow aufrufen können, anstatt immer wieder Integrationsskripte von Hand schreiben zu müssen.
Haben Sie bereits einen SJTU Zhiyuan No.1 API-Schlüssel beantragt, finden es aber immer noch schwierig, ihn in der Praxis tatsächlich zu nutzen?
Dieses Projekt existiert genau, um dieses Problem zu lösen:
Sie haben bereits API-Zugriff
Sie möchten ihn von Claude Code oder Codex aus nutzen
Aber der SJTU-Endpunkt selbst lässt sich nicht direkt in diese Agenten-Tools integrieren
Sie möchten die Integrationsschicht nicht jedes Mal neu schreiben
Related MCP server: Claude-LMStudio-Bridge
Unterstützt Claude Code
Unterstützt Codex
Unterstützt sowohl Text- als auch Vision-Aufgaben
Verwendet den OpenAI-kompatiblen SJTU-Endpunkt
Passt sich natürlich in bestehende MCP-Workflows ein
Für die meisten Benutzer ist der einfachste Weg:
git clone dieses Repository
cd in das Projektverzeichnis
Einmalig installieren
Als globalen MCP-Server in Claude Code oder Codex hinzufügen
git clone https://github.com/EternalWavee/sjtu-mcp.git
cd sjtu-mcp
pip install -e .Nach der Installation kann Ihr MCP-Client den Server bei Bedarf automatisch starten. Im normalen Gebrauch müssen Sie den Server-Befehl nicht jedes Mal manuell ausführen.
Erforderlich:
SJTU_API_KEY
Optional:
SJTU_API_BASE_URL
SJTU_DEFAULT_TEXT_MODEL
SJTU_DEFAULT_REASONING_MODEL
SJTU_DEFAULT_VISION_MODEL
SJTU_REQUEST_TIMEOUT
Wie man sie verwendet:
.env.example ist nur eine Vorlage, die zeigt, welche Variablen Sie benötigen
Fügen Sie diese Werte bei der tatsächlichen Verwendung in den env-Block Ihrer MCP-Konfiguration ein
Verwenden Sie dies, wenn sjtu in allen Ihren Claude Code-Projekten auf diesem Computer verfügbar sein soll.
claude mcp add sjtu --scope user -- python -m sjtu_mcp.serverDann:
Öffnen Sie ~/.claude.json
Suchen Sie den sjtu-Eintrag
Kopieren Sie den env-Abschnitt aus examples/claude-project.mcp.json
Ersetzen Sie your-api-key durch Ihren echten Schlüssel
Überprüfung:
claude mcp listVerwenden Sie dies, wenn Sie eine gemeinsame Konfiguration für Teamkollegen in das Repository einchecken möchten.
Anwendung:
Kopieren Sie examples/claude-project.mcp.json als .mcp.json in Ihr Projektstammverzeichnis
Ersetzen Sie your-api-key durch Ihren echten Schlüssel
Passen Sie bei Bedarf Standardmodelle und Timeout an
Windows / macOS Beispiel:
{
"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"
}
}
}
}Verwenden Sie dies, wenn Sie den Server nur für das aktuelle Projekt benötigen und die Konfiguration nicht einchecken möchten.
claude mcp add sjtu --scope local -- python -m sjtu_mcp.serverFügen Sie dann dieselben env-Werte zum entsprechenden MCP-Konfigurationseintrag hinzu.
Verwenden Sie dies, wenn sjtu in allen Ihren Codex-Projekten auf diesem Computer verfügbar sein soll.
codex mcp add sjtu -- python -m sjtu_mcp.serverDann:
Öffnen Sie Ihre eigene ~/.codex/config.toml
Kopieren Sie den Inhalt aus examples/codex-config.toml
Ersetzen Sie your-api-key durch Ihren echten Schlüssel
Speichern und laden Sie Codex neu oder laden Sie MCP neu
Überprüfung:
codex mcp listWenn Sie ~/.codex/config.toml bereits direkt verwalten, können Sie diese Vorlage verwenden:
[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
Standard für Zusammenfassungen, Umschreibungen, Bereinigungen und risikoarme Textaufgaben
minimax oder glm-5
nützlich für leichtgewichtiges Umschreiben, Klassifizierung oder Extraktion
deepseek-reasoner
besser für Aufgaben, die wirklich mehrstufiges logisches Denken erfordern
qwen3vl
ein starker Ausgangspunkt für Screenshots, OCR-artige Extraktion und Bildverständnis
qwen3coder
nützlich für code-nahe Hilfsaufgaben
Dieser Server geht derzeit davon aus, dass der SJTU-Endpunkt OpenAI-kompatible /models und /chat/completions unterstützt.
Lokale Bilder werden vor dem Senden als Daten-URLs kodiert.
Wenn Ihr Campus-Endpunkt modellspezifische Eigenheiten aufweist, erweitern Sie das Routing in src/sjtu_mcp/server.py.
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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