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MCP Video Recognition Server

by mario-andreschak
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TypeScript
Remote

MCP Video Recognition Server

MCP-сервер, который описывает изображения, транскрибирует аудио и резюмирует видео из локальных файлов. По умолчанию используется Google Gemini или любой OpenAI-совместимый эндпоинт, например OpenRouter.

Возможности

  • Выберите провайдера: Google Gemini (по умолчанию) или OpenAI-совместимый эндпоинт

  • Три MCP-инструмента для локальных изображений, аудио и видео

  • Опциональный запасной вариант модели Gemini, плюс финальный резервный OpenAI-совместимый вариант

Поддержка моделей различается. Выбор OpenAI-совместимого провайдера не означает, что каждый эндпоинт или модель обрабатывает все типы медиа, и сервер никогда не подменяет модель или провайдера за вас.

Related MCP server: Puter MCP Server

Предварительные требования

  • Node.js 18.0.0 или новее

  • Ключ API для вашего провайдера:

    • Gemini: GOOGLE_API_KEY

    • OpenAI-совместимый: OPENAI_COMPATIBLE_API_KEY

Установка

git clone https://github.com/yourusername/mcp-video-recognition.git
cd mcp-video-recognition
npm install
npm run build

Быстрый старт

Добавьте сервер в конфигурацию вашего MCP-клиента и укажите его на собранный dist/index.js:

{
  "mcpServers": {
    "video-recognition": {
      "command": "node",
      "args": ["/path/to/mcp-video-recognition/dist/index.js"],
      "env": {
        "GOOGLE_API_KEY": "your_google_api_key"
      }
    }
  }
}

В Windows используйте прямые слэши или удвоенные обратные слэши (\\) в пути. Сохраните файл и переподключите ваш MCP-клиент.

Для OpenRouter или другого OpenAI-совместимого эндпоинта установите RECOGNITION_PROVIDER=openai-compatible и его переменные. Готовый пример см. в Конфигурации.

С помощью FLUJO:

  1. Нажмите Add Server.

  2. Вставьте URL репозитория GitHub.

  3. Нажмите Parse, Clone, Install, Build and Save.

Конфигурация

Сервер считывает переменные окружения. Наиболее распространённые:

Переменная

По умолчанию

Назначение

RECOGNITION_PROVIDER

gemini

gemini или openai-compatible

GOOGLE_API_KEY

нет

Ключ API Gemini

GEMINI_MODEL

gemini-3.5-flash

Используемая модель Gemini

OPENAI_COMPATIBLE_API_KEY

нет

OpenAI-совместимый ключ API

OPENAI_COMPATIBLE_BASE_URL

нет

Базовый URL эндпоинта

OPENAI_COMPATIBLE_MODEL

xiaomi/mimo-v2.5

Используемая модель

ALLOWED_MEDIA_ROOTS

нет

Медиакаталоги для OpenAI-совместимого провайдера и резервного варианта Gemini

Некорректные значения останавливают запуск; они не исправляются молча.

Полный список переменных, правила валидации, пример для OpenRouter и поддерживаемые типы медиа см. в Справочнике по конфигурации.

О запасном варианте модели Gemini и финальном резервном варианте см. в Справочнике по восстановлению провайдера.

Инструменты

Сервер предоставляет три MCP-инструмента. Каждый принимает локальный filepath, необязательный prompt (по умолчанию Describe this content) и необязательный параметр modelname для переопределения.

  • image_recognition — описать изображение

  • audio_recognition — транскрибировать или описать аудио

  • video_recognition — описать видео

Пример:

{
  "name": "video_recognition",
  "arguments": {
    "filepath": "/path/to/video.mp4",
    "prompt": "Describe what happens in this video"
  }
}

Безопасность

  • HTTPS требуется по умолчанию. Обычный HTTP разрешён только для явно включённого локального эндпоинта.

  • OpenAI-совместимый провайдер и резервный вариант Gemini читают медиа только из каталогов, перечисленных в ALLOWED_MEDIA_ROOTS.

  • Ключи остаются в окружении процесса. Не коммитьте реальные ключи.

Правила эндпоинтов, лимиты ресурсов и порядок действий при инцидентах см. в Справочнике по безопасности.

Разработка

# 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

Структура проекта

  • src/index.ts: точка входа и создание провайдера

  • src/server.ts: MCP-сервер и транспорт

  • src/tools/: три инструмента распознавания

  • src/services/: провайдеры Gemini и OpenAI-совместимый

  • src/types/: общие типы

  • src/utils/: вспомогательные функции

Лицензия

MIT

Available Tools

3 tools
audio_recognitionB

Analyze and transcribe audio using Google Gemini AI

ParametersJSON Schema
NameRequiredDescriptionDefault
filepathYesPath to the media file to analyze
modelnameNoGemini model to use for recognitiongemini-2.0-flash
promptNoCustom prompt for the recognitionDescribe 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
NameRequiredDescriptionDefault
filepathYesPath to the media file to analyze
modelnameNoGemini model to use for recognitiongemini-2.0-flash
promptNoCustom prompt for the recognitionDescribe 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
NameRequiredDescriptionDefault
filepathYesPath to the media file to analyze
modelnameNoGemini model to use for recognitiongemini-2.0-flash
promptNoCustom prompt for the recognitionDescribe 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.

  1. 3 tool updatesv1.0.0
    • First observedaudio_recognition
    • First observedimage_recognition
    • First observedvideo_recognition
Deploy Server
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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

ActivitySlowing
ResponsivenessResponsive

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