XMZ MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| port | No | Port number when using SSE connection type | 3001 |
| Bucket | Yes | The name of the COS bucket (e.g., mybucket-123456) | |
| Region | Yes | The region where the COS bucket is located (e.g., ap-beijing) | |
| SecretId | Yes | Tencent Cloud COS secret ID for authentication | |
| SecretKey | Yes | Tencent Cloud COS secret key for authentication | |
| DatasetName | No | Optional parameter for data intelligent search operations. The name of the dataset for MateInsight. | |
| connectType | No | Connection type: 'stdio' (local) or 'sse' (remote) | stdio |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| getCosConfigC | 获取COS配置, 腾讯云配置 |
| putObjectC | 上传本地文件到存储桶 |
| putStringC | 上传字符串内容到存储桶 |
| putBase64C | 上传base64编码内容到存储桶 |
| putBufferC | 上传buffer内容到存储桶 |
| putObjectSourceUrlC | 通过 url下载文件并将文件上传到存储桶 |
| getObjectUrlC | 获取存储桶内的文件的带签名的下载链接 |
| getObjectC | 下载存储桶内的文件 |
| getBucketB | 查询存储桶内的文件列表 |
| imageInfoC | 图片处理-获取图片信息 |
| assessQualityC | 图片处理-图片质量评估 |
| aiSuperResolutionC | 图片处理-超分辨率 |
| aiPicMattingD | 图片处理-抠图 |
| aiQrcodeB | 图片处理-二维码识别-识别存储桶内二维码图片内容 |
| waterMarkFontC | 生成带文字水印的图片 |
| createMediaSmartCoverJobD | 创建媒体智能封面任务 |
| describeMediaJobB | 根据 jobid 查询指定的媒体智能封面任务结果 |
| imageSearchPicC | 根据输入的图片,从数据集中检索出与输入的图片内容相似的图片 |
| imageSearchTextC | 根据输入的文本内容,从数据集中检索出与输入的文本内容相符的图片 |
| createDocToPdfJobC | 创建文档转 pdf 处理任务 |
| describeDocProcessJobC | 根据 jobid 查询指定的文档转码任务结果 |
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 21 tools
Most tools have distinct purposes, with clear separation between image processing, file management, and job handling. However, there is some overlap between putBase64, putBuffer, putString, and putObject, which all handle file uploads with different input formats, potentially causing confusion.
Naming conventions are mixed, with some tools using camelCase (e.g., createDocToPdfJob) and others using snake_case (e.g., image_search_pic is implied but not consistent). While verbs like 'create', 'describe', 'get', and 'put' are used predictably, the overall pattern lacks uniformity.
With 21 tools, the count is on the higher side but reasonable for a server covering image processing, file storage, and job management. It feels slightly heavy but not excessive, as each tool serves a specific function within the domain.
The tool set provides good coverage for image processing and file operations, including upload, download, and management. Minor gaps exist, such as no direct delete or update tools for files or jobs, but agents can likely work around these with the available tools.