tokenhub-aigc-model
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MCP_AUTH_TOKEN | No | HTTP 模式可选鉴权，设置后 /mcp 请求必须携带 x-mcp-auth-token 请求头 | |
| TOKENHUB_MODEL | No | 兜底默认模型：仅当调用不传 model 时生效。推荐在对话中直接指定模型 | custom-model-og-v2 |
| TOKENHUB_API_KEY | Yes | TokenHub 控制台获取的 API Key，请求头 Authorization: Bearer <key> | |
| TOKENHUB_BASE_URL | No | 站点域名，末尾无需斜杠 | https://tokenhub.tencentmaas.com |
| TOKENHUB_TIMEOUT_MS | No | 单次请求超时（毫秒）。生图为同步接口，高画质/大尺寸生成耗时长，超时请调大（如 300000）；非法值自动回退默认 | 120000 |
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
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| tokenhub_generate_imageA | 调用 TokenHub 接口，根据文本描述同步生成图片（模型默认 custom-model-og-v2，需在 TokenHub 控制台开白并配置专属 API Key）。返回图片 URL 列表、request_id 与 token 用量。 |
| tokenhub_edit_imageA | 调用 TokenHub 接口，基于 1-16 张输入图 + 文本描述同步生成图片（模型默认 custom-model-og-v2，需开白）。支持局部重绘（mask）。输入图支持 URL 或 base64。返回图片 URL 列表、request_id 与 token 用量。 |
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 2 tools
The two tools have clearly distinct purposes: one generates an image purely from text, while the other edits/generates from 1-16 input images with optional masking. There is no realistic confusion between them.
Both tools follow the same tokenhub_<verb>_image pattern with static verb prefixes: edit and generate. The naming convention is consistent and predictable.
With only two tools, the server is slightly thin, but each tool covers a distinct core task in the image-generation domain. The focused scope makes the small count reasonable.
The two primary workflows—text-to-image generation and image editing/reinpainting—are covered, and the synchronous design avoids needing result-status tools. A minor gap is the lack of model-list or capability-discovery tooling, but agents can still complete the core tasks.