F
licenseNot graded
qualityD
maintenanceEnables text-to-image generation, style transfer, background removal, and automatic image cropping using Google's Imagen AI models through the Model Context Protocol.
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MCP Video Recognition Server
MCP Video Recognition Server
ローカルファイルから画像の説明、音声の文字起こし、動画の要約を行うMCPサーバーです。デフォルトではGoogle Geminiを使用し、OpenRouterなどのOpenAI互換エンドポイントにも対応しています。
特徴
プロバイダーを選択: Google Gemini（デフォルト）またはOpenAI互換エンドポイント
ローカルの画像、音声、動画に対応した3つのMCPツール
オプションのGeminiモデルフォールバックと、最終的なOpenAI互換バックアップ
モデルのサポートは異なります。OpenAI互換プロバイダーを選択しても、すべてのエンドポイントやモデルがすべてのメディアタイプを処理できるわけではありません。また、サーバーがモデルやプロバイダーを代わりに置き換えることはありません。
Related MCP server: Puter MCP Server
前提条件
Node.js 18.0.0以降
プロバイダーのAPIキー:
Gemini:
GOOGLE_API_KEYOpenAI互換:
OPENAI_COMPATIBLE_API_KEY
インストール
git clone https://github.com/yourusername/mcp-video-recognition.git
cd mcp-video-recognition
npm install
npm run buildクイックスタート
MCPクライアントの設定にサーバーを追加し、ビルドした dist/index.js を指定します:
{
"mcpServers": {
"video-recognition": {
"command": "node",
"args": ["/path/to/mcp-video-recognition/dist/index.js"],
"env": {
"GOOGLE_API_KEY": "your_google_api_key"
}
}
}
}Windowsでは、パスにフォワードスラッシュまたは二重バックスラッシュ（\\）を使用してください。ファイルを保存して、MCPクライアントに再接続します。
OpenRouterやその他のOpenAI互換エンドポイントを使用するには、RECOGNITION_PROVIDER=openai-compatible と関連変数を設定します。すぐに使える例については、構成 を参照してください。
FLUJO を使用する場合：
サーバーを追加 をクリックします。
GitHub URLを貼り付けます。
解析、クローン、インストール、ビルド、保存 をクリックします。
構成
サーバーは環境変数を読み取ります。最も一般的なものは次のとおりです:
変数 | デフォルト | 目的 |
|
|
|
| なし | Gemini APIキー |
|
| 使用するGeminiモデル |
| なし | OpenAI互換APIキー |
| なし | エンドポイントのベースURL |
| xiaomi/mimo-v2.5 | 使用するモデル |
| なし | OpenAI互換プロバイダーとGeminiバックアップ用のメディアディレクトリ |
不正な値があると起動が停止します。静かに修正されることはありません。
完全な変数リスト、検証ルール、OpenRouterの例、対応メディアタイプについては、構成リファレンス を参照してください。
Geminiモデルのフォールバックと最終バックアップについては、プロバイダーリカバリリファレンス を参照してください。
ツール
サーバーは3つのMCPツールを提供します。各ツールは、ローカルの filepath、オプションの prompt（デフォルトは Describe this content）、オプションの modelname 上書きを受け取ります。
image_recognition— 画像を説明するaudio_recognition— 音声を文字起こしまたは説明するvideo_recognition— 動画を説明する
例:
{
"name": "video_recognition",
"arguments": {
"filepath": "/path/to/video.mp4",
"prompt": "Describe what happens in this video"
}
}セキュリティ
デフォルトではHTTPSが必要です。プレーンなHTTPは、明示的に有効化されたローカルエンドポイントでのみ許可されます。
OpenAI互換プロバイダーとGeminiバックアップは、
ALLOWED_MEDIA_ROOTSにリストしたディレクトリからのみメディアを読み取ります。キーはプロセス環境に保持されます。実際のキーをコミットしないでください。
エンドポイントのルール、リソース制限、インシデント対応については、セキュリティリファレンス を参照してください。
開発
# Run in development mode
GOOGLE_API_KEY=your_api_key npm run dev
# Build and run the provider foundation tests
npm run verify:provider-foundationプロジェクト構造
src/index.ts: エントリポイントとプロバイダー構築src/server.ts: MCPサーバーとトランスポートsrc/tools/: 3つの認識ツールsrc/services/: GeminiおよびOpenAI互換プロバイダーsrc/types/: 共有タイプsrc/utils/: ヘルパー
ライセンス
MIT
Available Tools
3 toolsaudio_recognitionB
Analyze and transcribe audio using Google Gemini AI
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Path to the media file to analyze | |
| modelname | No | Gemini model to use for recognition | gemini-2.0-flash |
| prompt | No | Custom prompt for the recognition | Describe this content |
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden of behavioral disclosure. It only states 'analyze and transcribe' but does not detail output format, processing behavior, authentication needs, or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the core purpose. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain what the tool returns (e.g., transcribed text or analysis). It does not, nor does it cover edge cases or prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description does not add additional meaning beyond what the schema provides, meeting the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: analyze and transcribe audio using Google Gemini AI. It explicitly mentions 'audio' which distinguishes it from sibling tools image_recognition and video_recognition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs alternatives (e.g., image_recognition, video_recognition). The description only states what it does without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
image_recognitionB
Analyze and describe images using Google Gemini AI
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Path to the media file to analyze | |
| modelname | No | Gemini model to use for recognition | gemini-2.0-flash |
| prompt | No | Custom prompt for the recognition | Describe this content |
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only mentions 'using Google Gemini AI' but does not disclose safety (e.g., read-only vs destructive), API costs, file size limits, or the nature of the analysis (e.g., real-time, batch).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with verb and resource. No wasted words. Efficient and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, yet the description does not explain what the tool returns (e.g., text description, confidence scores). For a tool with 3 parameters and no annotations, this leaves the agent guessing about the response format and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with each parameter described. The description adds no additional meaning beyond the schema; it only names the AI provider. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Analyze and describe images') and the technology ('using Google Gemini AI'). It distinguishes from sibling tools (audio_recognition, video_recognition) by specifying the media type (images).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., audio_recognition, video_recognition). No mention of prerequisites, limitations, or scenarios where it is not appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
video_recognitionC
Analyze and describe videos using Google Gemini AI
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Path to the media file to analyze | |
| modelname | No | Gemini model to use for recognition | gemini-2.0-flash |
| prompt | No | Custom prompt for the recognition | Describe this content |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool analyzes and describes videos but doesn't mention critical behavioral aspects like rate limits, authentication requirements, file size limits, supported video formats, processing time, or error handling. The description is too vague about what 'analyze and describe' entails operationally.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential information, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of video analysis (which typically involves format handling, processing time, and potential errors), no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how to interpret results, or any operational constraints, leaving significant gaps for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how the prompt interacts with video analysis or model selection trade-offs. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as analyzing and describing videos using Google Gemini AI, which is specific (verb+resource) and distinguishes it from sibling tools like audio_recognition and image_recognition. However, it doesn't explicitly mention video-specific capabilities beyond the name, leaving some ambiguity about whether it handles all video formats or specific features.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus its siblings (audio_recognition, image_recognition). It doesn't mention prerequisites, limitations, or alternative scenarios, leaving the agent to infer usage based on tool names alone.
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
v1.0.0- First observed
audio_recognition - First observed
image_recognition - First observed
video_recognition
TDQS
B3.2/5.0
Scored across 3 tools
Disambiguation5/5
Each tool has a clearly distinct purpose targeting different media types: audio, images, and videos. There is no overlap in functionality, as they handle separate input formats with similar analysis capabilities but different domains.
Naming Consistency5/5
All tool names follow a consistent pattern of 'media_type_recognition' using snake_case. This predictable naming scheme makes it easy to understand what each tool does based on its name alone.
Tool Count3/5
With only 3 tools, the server feels somewhat thin for a video recognition domain, as it lacks operations like video editing, frame extraction, or metadata retrieval. However, the core recognition functions for audio, images, and videos are covered, making it borderline appropriate.
Completeness3/5
The server provides basic recognition for three media types but lacks comprehensive coverage for video processing. There are no tools for operations like video segmentation, object tracking, or format conversion, which are common in video recognition workflows, leaving notable gaps.
Maintenance
ActivityMaintained
ResponsivenessResponsive
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