@avclabs.ai/enhance-mcp
Official@avclabs.ai/enhance-mcp (Node.js)
中文文档 | English
MCPプロトコルに基づいた動画高品質化サービスであり、FastAPI HTTPサーバーと対話するためのMCPクライアントサーバーとして機能します。
機能
以下のMCPツールが提供されます:
create_task- 動画高品質化タスクの作成(URLまたはローカルファイルのアップロードをサポート)get_task_status- タスクステータスの照会enhance_video_sync- 動画の同期的な高品質化(完了まで待機)
インストール
npmからインストール(推奨)
npm install -g @avclabs.ai/enhance-mcpまたはyarn/pnpmを使用する場合:
yarn global add @avclabs.ai/enhance-mcp
pnpm add -g @avclabs.ai/enhance-mcpソースからインストール
git clone https://github.com/avclabs/enhance-mcp.git
cd js_client
npm install
npm run build使用方法
1. コマンドライン
グローバルインストール後に直接使用します:
avclabs-enhance-mcp --base-url https://mcp.avc.ai --api-key your-api-keyまたは環境変数を使用します:
# Windows PowerShell
$env:HTTP_API_BASE_URL="https://mcp.avc.ai"
$env:HTTP_API_KEY="your-api-key"
avclabs-enhance-mcp
# Windows CMD
set HTTP_API_BASE_URL=https://mcp.avc.ai
set HTTP_API_KEY=your-api-key
avclabs-enhance-mcp
# macOS/Linux
export HTTP_API_BASE_URL=https://mcp.avc.ai
export HTTP_API_KEY=your-api-key
avclabs-enhance-mcp2. Claude Desktopでの設定
Claude Desktopの設定ファイルを編集します:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"video-enhancement": {
"command": "avclabs-enhance-mcp",
"args": [
"--base-url",
"https://mcp.avc.ai",
"--api-key",
"your-api-key"
]
}
}
}3. npxで使用する(グローバルインストール不要)
npx @avclabs.ai/enhance-mcp --base-url https://mcp.avc.ai --api-key your-api-keyClaude Desktopの設定:
{
"mcpServers": {
"video-enhancement": {
"command": "npx",
"args": [
"@avclabs.ai/enhance-mcp",
"--base-url",
"https://mcp.avc.ai",
"--api-key",
"your-api-key"
]
}
}
}提供ツール
create_task
動画高品質化タスクを作成します(非同期)。
パラメータ:
video_source(string, 必須): 動画URLまたはローカルファイルパスtype(string, オプション): アップロードタイプ、デフォルトは "url"オプション:
"url"- ネットワーク動画URL,"local"- ローカルファイルパス
resolution(string, オプション): ターゲット解像度、デフォルトは 720pオプション: 480p, 540p, 720p, 1080p, 2k
例:
// URL mode
{
"video_source": "https://example.com/video.mp4",
"type": "url",
"resolution": "1080p"
}
// Local file mode
{
"video_source": "/path/to/local/video.mp4",
"type": "local",
"resolution": "1080p"
}戻り値:
{
"success": true,
"task_id": "xxx",
"status": "wait"
}get_task_status
タスクステータスを照会します。
パラメータ:
task_id(string, 必須): タスクID
例:
{
"task_id": "task-123-abc"
}戻り値:
{
"success": true,
"task_id": "xxx",
"status": "completed",
"progress": 100,
"video_url": "https://...",
"error_message": null,
"created_at": "2024-01-01T00:00:00Z",
"updated_at": "2024-01-01T00:01:00Z"
}enhance_video_sync
動画を同期的に高品質化します(完了までブロックして待機)。
パラメータ:
video_source(string, 必須): 動画URLまたはローカルファイルパスtype(string, オプション): アップロードタイプ、デフォルトは "url"オプション:
"url"- ネットワーク動画URL,"local"- ローカルファイルパス
resolution(string, オプション): ターゲット解像度、デフォルトは 720ppoll_interval(number, オプション): ポーリング間隔(秒)、デフォルトは 5timeout(number, オプション): タイムアウト(秒)、デフォルトは 600
例:
{
"video_source": "https://example.com/video.mp4",
"type": "url",
"resolution": "1080p",
"poll_interval": 5,
"timeout": 600
}戻り値:
{
"success": true,
"task_id": "xxx",
"status": "completed",
"progress": 100,
"video_url": "https://..."
}ファイルアップロードに関する注意点
type が "local" に設定されている場合、MCPサーバーは以下の処理を行います:
ローカルファイルを読み込む
ファイルをbase64でエンコードする
動画高品質化サービスにアップロードする
制限事項:
最大ファイルサイズ: 100MB
環境変数
変数 | 説明 | デフォルト |
| FastAPI HTTPサーバーのアドレス |
|
| API認証キー | なし |
開発
# Clone the repository
git clone https://github.com/avclabs/enhance-mcp.git
cd js_client
# Install dependencies
npm install
# Development mode (auto-compile)
npm run dev
# Build
npm run buildライセンス
MITライセンス - 詳細は LICENSE ファイルを参照してください。
Available Tools
3 toolscreate_taskB
创建视频增强任务(异步)
支持两种上传方式:
URL 上传:提供视频 URL
本地上传:提供本地文件路径,MCP Server 自动上传到 TOS 对象存储
参数说明:
video_source: 视频 URL 或本地文件路径
type: "url" 或 "local"
resolution: 目标分辨率
| Name | Required | Description | Default |
|---|---|---|---|
| video_source | Yes | 视频URL地址或本地文件路径 | |
| type | No | 上传类型:url=网络视频,local=本地文件 | url |
| resolution | No | 目标分辨率,默认720p | 720p |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It notes the task is async, but fails to mention authentication needs, rate limits, or the return format (e.g., task ID). It also does not disclose any side effects or failure behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description uses bullet points and is relatively concise. Each paragraph serves a purpose, though some wording could be tightened.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the async nature and upload methods, but it lacks return value details (no output schema) and does not mention error cases or post-creation steps. Given the tool's complexity, it is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description adds value by explaining that local files are automatically uploaded to TOS storage, which is not in the schema. It also summarizes parameter meanings clearly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates an asynchronous video enhancement task and lists two upload methods. It implicitly differentiates from the sibling 'enhance_video_sync' which is synchronous, but does not explicitly name the sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the two upload methods and parameter defaults, but does not provide explicit guidance on when to use this tool versus siblings, nor does it state prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
enhance_video_syncA
同步增强视频(阻塞等待完成)
支持两种上传方式:
URL 上传:提供视频 URL
本地上传:提供本地文件路径,MCP Server 自动上传到 TOS 对象存储
参数说明:
video_source: 视频 URL 或本地文件路径
type: "url" 或 "local"
resolution: 目标分辨率
poll_interval: 轮询间隔(秒)
timeout: 超时时间(秒)
| Name | Required | Description | Default |
|---|---|---|---|
| video_source | Yes | 视频URL地址或本地文件路径 | |
| type | No | 上传类型:url=网络视频,local=本地文件 | url |
| resolution | No | 目标分辨率,默认720p | 720p |
| poll_interval | No | 轮询间隔(秒),默认5 | |
| timeout | No | 超时时间(秒),默认600 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses blocking behavior and automatic upload to TOS for local files, but lacks details on error handling, side effects, or required permissions. Without annotations, the description carries the full burden but still provides moderate transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a clear front-loaded purpose and well-structured bullet points for parameters. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all 5 parameters, behavior (blocking), and upload methods. It omits expected return value and prerequisites, but given the lack of output schema, it is sufficiently complete for invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value by grouping parameters and explaining the two upload methods, which goes beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool synchronously enhances video and blocks until completion. It distinguishes itself from siblings like create_task and get_task_status by focusing specifically on video enhancement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly guide when to use this tool over alternatives, nor does it mention when not to use it. However, the context of video enhancement is clear, and the sibling tools handle different tasks, so usage is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_task_statusA
查询视频增强任务状态
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | 任务ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only states the purpose without disclosing any behavioral traits such as polling requirements, rate limits, or expected response behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words; efficient and to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple status query with one parameter, the description is mostly complete but could benefit from mentioning possible return statuses or output format since no output schema is provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds no additional meaning beyond what is already in the input schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'query' and the resource 'video enhancement task status', distinguishing from sibling tools 'create_task' and 'enhance_video_sync' which have different actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives, but the context of sibling tools implies it is for checking status after creation or enhancement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
v0.1.18- First observed
create_task - First observed
enhance_video_sync - First observed
get_task_status
TDQS
Scored across 3 tools
Each tool has a distinct purpose: creating an async task, performing a synchronous enhancement, and querying task status. No overlap or ambiguity.
All names use snake_case and verb_noun pattern, but the verbs vary (create, enhance, get) and 'enhance_video_sync' mixes a descriptor. Mostly consistent.
Three tools is on the lower end but reasonable for a focused video enhancement service. Each tool serves a clear function without being redundant.
Covers creation and status checking, but lacks cancellation, listing all tasks, or retrieving final results explicitly. Minor but notable gaps.
Maintenance
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