ModelScope MCP Server
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MODELSCOPE_API_TOKEN | Yes | Your ModelScope API token. Obtain from modelscope.cn. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_current_userA | 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_environment_infoA | 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_modelsC | Search for models on ModelScope. |
| search_datasetsA | Search for datasets on ModelScope. |
| search_studiosB | Search for studios on ModelScope. |
| search_papersB | Search for papers on ModelScope. |
| search_mcp_serversA | Search for MCP servers on ModelScope. |
| get_mcp_server_detailA | Get detailed information about a specific MCP server. |
| generate_imageA | Generate an image based on the given text prompt and ModelScope AIGC model ID. Supports both text-to-image and image-to-image generation. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Each tool targets a distinct resource: user, environment, models, datasets, studios, papers, MCP servers, and image generation. There is no overlap between search actions, and get_mcp_server_detail is clearly a follow-up to search_mcp_servers.
All tools follow a consistent verb_noun pattern with lowercase snake_case: get_*, search_*, and generate_image. No mixed conventions or vague verbs.
With 9 tools, the server is well-scoped. The count covers user info, environment info, search across five content types, a detail fetch for MCP servers, and image generation—each earning its place.
The search tools cover discovery for models, datasets, studios, and papers, but only MCP servers have a dedicated detail endpoint. This creates a notable gap: after searching for a model or dataset, there is no way to fetch full details, which could hinder workflows that require specific resource metadata.