mcp-multimedia-server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MCP_PORT | No | Port for SSE transport (optional) | |
| MCP_TRANSPORT | No | Transport mode, e.g., 'sse' (optional) | |
| MCP_MEDIA_MODEL | No | Model name to use | mimo-v2.5 |
| MCP_MEDIA_API_KEY | Yes | API key for the OpenAI compatible endpoint | |
| MCP_MEDIA_BASE_URL | No | OpenAI compatible endpoint URL | https://opencode.ai/zen/go/v1 |
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": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_imageA | 图片理解/OCR：分析一张或多张图片，支持描述、文字识别(OCR)、图表/UI/报错诊断等。单张用 image，多张（如文档多页）用 image_batch 数组。可用 preset 选任务类型(describe/ocr/chart/ui/diagram/error)或自由写 prompt。本地图片自动压缩优化（OCR 足够清晰、更快更省），需原图细节可设 compress=false。 |
| analyze_videoA | 视频理解：让模型描述视频内容、按时间顺序总结。支持本地文件、URL 或 base64，本地视频自动压缩。fps 控制抽帧密度（默认 2，范围 1-30；越大时序越精细、越耗 token），一般用默认即可。 |
| analyze_audioA | 音频理解/STT：转录或总结一段音频内容。推荐用本地文件或 base64（URL 音频可能不被识别）。不传 prompt 默认转录并总结主旨。 |
| asrA | 语音转写(ASR)：把音频转成纯文本，适合会议记录、方言、嘈杂环境录音。仅支持 wav/mp3，最大 10MB。用 language 明确语种(auto/zh/en)可提高准确率。 |
| image_metadataC | 读取本地图片的元信息（尺寸、格式、颜色模式、EXIF/GPS）。需本地文件路径，不调用云端。 |
| get_server_statusA | 查询服务状态：云端模型是否已配置、当前模型与各项限制/处理参数。 |
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 6 tools
Most tools are clearly distinct by media type (video, audio, image, status). The main overlap is between analyze_audio and asr, both handling audio transcription, but asr is specifically pure text with format/size limits, while analyze_audio offers broader understanding and summarization, making the distinction functional.
There is a strong pattern with analyze_video, analyze_audio, analyze_image, but asr and image_metadata break the verb_noun convention. Mixed styles (acronym, noun_phrase) and vague naming like asr make it less predictable.
Six tools cover the essential multimedia operations (audio/video/image analysis, speech transcription, metadata, status) without redundancy. The count is well-scoped for a multimedia server, each tool serving a clear purpose.
The tool surface covers major analysis needs: video, audio, image understanding, pure transcription, and metadata for images. Minor gaps exist like no metadata extraction for audio/video or a dedicated video transcription tool, but these are not critical for the apparent domain.