What do I need to run Modelscope Mcp Server?
A valid ModelScope API token (set as `MODELSCOPE_API_TOKEN`), Python with `uv` or Docker to run the server, and an MCP-compatible client for interaction.
@modelscope
Basic information
A MCP server that integrates with ModelScope's ecosystem, providing seamless access to AI models, datasets, apps, papers, and generation capabilities through popular MCP clients.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"modelscope-mcp-server": {
"command": "uvx",
"args": [
"modelscope-mcp-server"
],
"env": {
"MODELSCOPE_API_TOKEN": "your-api-token"
}
}
}
}Get current authenticated user information from ModelScope. Use this when a request is about the user's own profile for ModelScope. Or when information is missing to build other tool calls.
Get current MCP server environment information. Returns version information for the server, FastMCP framework, MCP protocol, and Python runtime. Useful for debugging and compatibility checking.
Search for models on ModelScope.
Search for datasets on ModelScope.
Search for studios on ModelScope.
Search for papers on ModelScope.
Search for MCP servers on ModelScope.
Get detailed information about a specific MCP server.
Generate an image based on the given text prompt and ModelScope AIGC model ID. Supports both text-to-image and image-to-image generation.
A Model Context Protocol (MCP) server that integrates with the ModelScope ecosystem, providing seamless access to AI models, datasets, apps, papers, and generation capabilities through popular MCP clients.
Install and run via uvx modelscope-mcp-server or the pre-built Docker image spadrian/modelscope-mcp-server. Configure your MCP client (e.g., Claude Desktop, Cursor, VS Code, Cherry Studio) with the appropriate JSON, setting the environment variable MODELSCOPE_API_TOKEN to your ModelScope API token. The server supports stdio (default), HTTP, and SSE transports; for HTTP/SSE use --transport http and optionally --port.
A valid ModelScope API token (set as `MODELSCOPE_API_TOKEN`), Python with `uv` or Docker to run the server, and an MCP-compatible client for interaction.
It supports stdio (default), and HTTP/SSE transports. Use `--transport http` or `--transport sse` to switch, and optionally a custom port with `--port`.
All data (models, papers, user info) is fetched from the [ModelScope](https://modelscope.cn) cloud platform. No local storage is used.
Yes, you must provide a ModelScope API token via the `MODELSCOPE_API_TOKEN` environment variable.
The README does not list any explicit limitations. Some features (documentation search, Gradio API integration) are marked as coming soon and are not yet available.
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