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Youtube Vision MCP

by minbang930
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YouTube Vision MCP サーバー ( youtube-vision )

NPMバージョン ライセンス: MIT 鍛冶屋のバッジ

Google Gemini Vision API を利用して YouTube 動画と連携する MCP（Model Context Protocol）サーバー。ユーザーは YouTube 動画から説明、要約、質問への回答、重要な場面の抽出などを取得できます。

特徴

  • Gemini Vision API を使用して YouTube ビデオを分析します。

  • さまざまなインタラクションに対応する複数のツールを提供します。

    • 一般的な説明またはQ&A（ ask_about_youtube_video ）

    • 要約（ summarize_youtube_video ）

    • キーモーメント抽出 ( extract_key_moments )

  • generateContentをサポートする利用可能な Gemini モデルを一覧表示します。

  • 環境変数を介して構成可能な Gemini モデル。

  • stdio (標準入出力) を介して通信します。

Related MCP server: YouTube Insights MCP Server

前提条件

このサーバーを使用する前に、次のものを用意してください。

  • **Node.js:**バージョン18以上を推奨。nodejs.orgからダウンロードできます。

  • Google Gemini API キー: Google AI Studioまたは Google Cloud Console から API キーを取得します。

インストールと使用方法

このサーバーを使用するには、主に 2 つの方法があります。

Smithery経由でインストール

Smithery経由で Claude Desktop 用の youtube-vision-mcp を自動的にインストールするには:

npx -y @smithery/cli install @minbang930/youtube-vision-mcp --client claude

オプション 1: npx を使用する (簡単な使用に推奨)

このサーバーを実行する最も簡単な方法はnpxを使用することです。これにより、永続的なインストールを必要とせずにパッケージをダウンロードして実行できます。

MCP クライアントの設定ファイル (Claude、VSCode など) 内で構成できます。

{
  "mcpServers": {
    "youtube-vision": {
      "command": "npx",
      "args": [
        "-y",
        "youtube-vision"
      ],
      "env": {
        "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY",
        "GEMINI_MODEL_NAME": "gemini-2.0-flash"
      }
    }
  }
}

"YOUR_GEMINI_API_KEY"実際の Google Gemini API キーに置き換えます。

オプション2: 手動インストール（ソースから）

コードを変更したり、ソースから直接実行したりする場合は、次の手順を実行します。

  1. リポジトリをクローンします。

    git clone https://github.com/minbang930/Youtube-Vision-MCP.git
cd youtube-vision

  2. 依存関係をインストールします:

    npm install

  3. プロジェクトをビルドします。

    npm run build

  4. **構成と実行:**その後、コンパイルされたコードをnode dist/index.jsを使用して直接実行できます ( GEMINI_API_KEY環境変数として設定されていることを確認してください)。または、MCP クライアントを構成して、 nodeコマンドとdist/index.jsへの絶対パスを使用してコードを実行し、npx の例に示すようにenv設定を介して API キーを渡します。

構成

サーバーは次の環境変数を使用します。

  • GEMINI_API_KEY (必須): Google Gemini API キー。

  • GEMINI_MODEL_NAME （オプション）：使用するGeminiモデル（例： gemini-1.5-flash ）。デフォルトはgemini-2.0-flashです。**重要：**本番環境または商用利用の場合は、「試験運用版」または「プレビュー版」とマークされていないモデルバージョンを選択してください。

環境変数は、MCP クライアントの設定ファイル (例: mcp_settings.json ) のenvセクションで設定する必要があります。

利用可能なツール

1. ask_about_youtube_video

ビデオに関する質問に答えます。質問がない場合は、一般的な説明を提供します。

  • 入力：

    • youtube_url (文字列、必須): YouTube ビデオの URL。

    • question (文字列, オプション): 動画について尋ねる具体的な質問。省略した場合は、一般的な説明が生成されます。

  • **出力:**回答または説明を含むテキスト。

2. summarize_youtube_video

指定された YouTube 動画の概要を生成します。

  • 入力：

    • youtube_url (文字列、必須): YouTube ビデオの URL。

    • summary_length (文字列、オプション): 希望する要約の長さ ('short'、'medium'、'long')。デフォルトは 'medium' です。

  • **出力:**ビデオの概要を含むテキスト。

3. 重要なextract_key_moments

指定された YouTube 動画から重要な瞬間 (タイムスタンプと説明) を抽出します。

  • 入力：

    • youtube_url (文字列、必須): YouTube ビデオの URL。

    • number_of_moments （整数、オプション）：抽出するキーモーメントの数。デフォルトは3です。

  • **出力:**タイムスタンプ付きの重要な瞬間を説明するテキスト。

4. list_supported_models

generateContentメソッドをサポートする利用可能な Gemini モデルを一覧表示します (REST API 経由で取得)。

  • **入力:**なし

  • **出力:**サポートされているモデル名をリストしたテキスト。

重要な注意事項

  • **本番環境向けモデルの選択：**このサーバーを本番環境または商用目的で使用する場合は、選択したGEMINI_MODEL_NAMEが本番環境に適した安定したバージョンであることを確認してください。Gemini API利用規約に従い、「試験運用版」または「プレビュー版」とマークされたモデルは本番環境での展開は許可されていません。

  • **API利用規約：**このサーバーの使用はGoogle Gemini APIに依存しています。ユーザーはGoogle API利用規約およびGemini API追加利用規約を確認し、遵守する責任があります。Gemini APIの無料プランと有料プランでは、データ利用ポリシーが異なる場合がありますのでご注意ください。無料プランをご利用の際は、機密情報や機密情報を送信しないでください。

  • コンテンツに関する責任： Gemini API を通じて生成されるコンテンツの正確性と適切性は保証されません。生成されたコンテンツを利用または公開する前に、ご自身の判断で判断してください。

ライセンス

このプロジェクトはMITライセンスの下で提供されています。詳細はLICENSEファイルをご覧ください。

Available Tools

4 tools
ask_about_youtube_videoB

Answers a question about the video or provides a general description if no question is asked.

ParametersJSON Schema
NameRequiredDescriptionDefault
youtube_urlYes
questionNoQuestion about the video content. If omitted, a general description will be generated.

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool can answer questions or provide descriptions, but doesn't cover important aspects like rate limits, authentication needs, error conditions, or what constitutes a 'general description.' This leaves significant gaps for a tool that presumably processes external content.

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 communicates the core functionality without any wasted words. It's appropriately sized for a tool with two parameters and front-loads the essential information about what the tool does.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description provides basic purpose and parameter guidance but lacks sufficient detail about behavioral traits, error handling, or output format. For a tool that interacts with external video content, more context about limitations or capabilities would be helpful to achieve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50% (only the question parameter has a description). The description adds valuable context by explaining that omitting the question parameter triggers a general description, which clarifies the optional nature and default behavior. This compensates well for the schema's partial coverage.

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: answering questions about YouTube videos or providing general descriptions. It specifies the verb ('answers'/'provides') and resource ('video'), but doesn't explicitly differentiate from sibling tools like summarize_youtube_video or extract_key_moments, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool (for questions about video content or general descriptions) but doesn't provide explicit guidance on when to choose alternatives like summarize_youtube_video or extract_key_moments. No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

extract_key_momentsC

Extracts key moments (timestamps and descriptions) from a given YouTube video.

ParametersJSON Schema
NameRequiredDescriptionDefault
youtube_urlYes
number_of_momentsNoNumber of key moments to extract (default: 3).

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 full burden but offers minimal behavioral insight. It mentions what is extracted ('timestamps and descriptions') but doesn't cover critical aspects like error handling (e.g., invalid URLs, unsupported videos), performance (e.g., processing time), or output format details. This is inadequate for a tool with potential complexity.

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 directly states the tool's function without unnecessary words. It is front-loaded with the core action and resource, making it easy 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 no annotations, no output schema, and incomplete parameter documentation (50% coverage), the description is insufficient. It lacks details on behavioral traits, error conditions, and output structure, which are essential for an extraction tool. The description does not compensate for these gaps in structured data.

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 50% (only 'number_of_moments' has a description), and the description adds no parameter-specific information beyond implying a YouTube URL is needed. It doesn't clarify URL format requirements or the nature of 'key moments,' leaving gaps in understanding the 'youtube_url' parameter. Baseline 3 is appropriate given partial schema coverage.

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 action ('extracts') and resource ('key moments from a given YouTube video'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'summarize_youtube_video' or 'ask_about_youtube_video', which might also process video content in different ways.

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 alternatives. It doesn't mention when to choose extraction over summarization or querying, nor does it specify prerequisites or constraints (e.g., video length, availability). This leaves the agent without context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_supported_modelsB

Lists available Gemini models that support the 'generateContent' method.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It lacks details on permissions, rate limits, output format, or whether it's a read-only operation. 'Lists' implies a safe read, but this isn't explicitly confirmed, leaving behavioral gaps.

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 directly states the tool's purpose without redundancy. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, with no wasted verbiage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate but incomplete. It explains what the tool does but lacks context on why to use it, output details, or behavioral traits. For a list operation, this is the bare minimum, scoring a 3.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for zero parameters, as no compensation is needed.

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 action ('Lists') and resource ('available Gemini models'), specifying they support the 'generateContent' method. It distinguishes from sibling tools (YouTube-related) by focusing on model listing rather than video processing. However, it doesn't explicitly differentiate from hypothetical model-related siblings, keeping it at 4 rather than 5.

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 alternatives. It doesn't mention prerequisites, context for selecting models, or relationships to sibling tools. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

summarize_youtube_videoC

Generates a summary of a given YouTube video URL using Gemini Vision API.

ParametersJSON Schema
NameRequiredDescriptionDefault
youtube_urlYes
summary_lengthNoDesired summary length: 'short', 'medium', or 'long' (default: 'medium').medium

TDQS

C2.9/5.0
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 of behavioral disclosure. It mentions the method ('using Gemini Vision API') but lacks details on rate limits, authentication needs, error handling, or output format. For a tool that likely involves API calls and video processing, this is a significant gap in transparency.

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 front-loads the core purpose without unnecessary details. It's appropriately sized for the tool's complexity, with zero waste or redundancy, making it easy to understand at a glance.

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 tool's complexity (involving video processing and an external API), lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't cover behavioral aspects like performance, limitations, or what the summary output looks like, leaving significant gaps for effective tool use.

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 50% (only 'summary_length' has a description). The description adds no parameter semantics beyond the schema, as it doesn't explain the 'youtube_url' parameter or provide additional context for 'summary_length'. With partial schema coverage, the description doesn't compensate for the undocumented parameter, resulting in a baseline score.

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: 'Generates a summary of a given YouTube video URL using Gemini Vision API.' It specifies the verb ('Generates a summary'), resource ('YouTube video URL'), and method ('using Gemini Vision API'). However, it doesn't explicitly differentiate from sibling tools like 'ask_about_youtube_video' or 'extract_key_moments', which might offer similar or overlapping functionality.

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 alternatives. It doesn't mention sibling tools or contexts where this tool is preferred, such as for quick overviews versus detailed analysis. Without such guidance, users might struggle to choose between this and tools like 'ask_about_youtube_video' or 'extract_key_moments'.

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. 4 tool updates
    • First observedask_about_youtube_video
    • First observedextract_key_moments
    • First observedlist_supported_models
    • First observedsummarize_youtube_video
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TDQS

B3.1/5.0

Scored across 4 tools

Disambiguation4/5

The tools have mostly distinct purposes, but 'ask_about_youtube_video' and 'summarize_youtube_video' could be confused as both provide descriptive outputs about video content. However, 'ask_about_youtube_video' is question-driven while 'summarize_youtube_video' is general, and the other tools ('extract_key_moments', 'list_supported_models') are clearly differentiated.

Naming Consistency3/5

Three tools follow a consistent verb_noun pattern ('ask_about_youtube_video', 'extract_key_moments', 'summarize_youtube_video'), but 'list_supported_models' deviates by using 'list' instead of a more descriptive verb like 'get' or 'retrieve', and it lacks the 'youtube_video' domain specificity. This mixed convention reduces predictability.

Tool Count4/5

With 4 tools, the count is reasonable for a focused YouTube video analysis server. It covers core functionalities like description, summarization, moment extraction, and model listing, though it could be slightly expanded for more comprehensive coverage (e.g., adding video metadata retrieval).

Completeness3/5

The server covers key video analysis tasks (description, summarization, moment extraction) and model support listing, but there are notable gaps. For example, it lacks tools for video metadata (e.g., title, duration, uploader), search capabilities, or interaction with YouTube's API beyond vision-based analysis, which limits agent workflows in broader YouTube contexts.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

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