What can you do with this server?

The Youtube Vision MCP server leverages the Google Gemini Vision API to analyze and interact with YouTube videos. You can: * Generate video summaries with customizable length options ('short', 'medium', 'long') * Ask questions about video content or get a general description * Extract key moments with timestamps from videos * List supported Gemini models compatible with content generation

Which integrations are available for this server?

Leverages the Gemini Vision API to process and analyze YouTube video content, with support for multiple Gemini models that can be configured via environment variables. Analyzes YouTube videos using the Gemini Vision API to provide descriptions, answers to questions, summaries, and extraction of key moments with timestamps.

How do I use Youtube Vision MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Youtube Vision MCP summarize this video about AI advancements in 2024" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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Youtube Vision MCP

by minbang930

Overview Schema Related Servers Score Discussions

JavaScript

Remote

YouTube Vision MCP Server (`youtube-vision`)

NPM version License: MIT

MCP (Model Context Protocol) server that utilizes the Google Gemini Vision API to interact with YouTube videos. It allows users to get descriptions, summaries, answers to questions, and extract key moments from YouTube videos.

Features

Analyzes YouTube videos using the Gemini Vision API.
Provides multiple tools for different interactions:
- General description or Q&A (ask_about_youtube_video)
- Summarization (summarize_youtube_video)
- Key moment extraction (extract_key_moments)
Lists available Gemini models supporting generateContent.
Configurable Gemini model via environment variable.
Communicates via stdio (standard input/output).

Related MCP server: YouTube Insights MCP Server

Prerequisites

Before using this server, ensure you have the following:

Node.js: Version 18 or higher recommended. You can download it from nodejs.org.
Google Gemini API Key: Obtain your API key from Google AI Studio or Google Cloud Console.

Installation & Usage

There are two main ways to use this server:

Installing via Smithery

To install youtube-vision-mcp for Claude Desktop automatically via Smithery:

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

Option 1: Using npx (Recommended for quick use)

The easiest way to run this server is using npx, which downloads and runs the package without needing a permanent installation.

You can configure it within your MCP client's settings file (Claude, VSCode .. ):

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

Replace "YOUR_GEMINI_API_KEY" with your actual Google Gemini API key.

Option 2: Manual Installation (from Source)

If you want to modify the code or run it directly from the source:

Clone the repository:

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

Install dependencies:
```
npm install
```
Build the project:
```
npm run build
```
Configure and run: You can then run the compiled code using node dist/index.js directly (ensure GEMINI_API_KEY is set as an environment variable) or configure your MCP client to run it using the node command and the absolute path to dist/index.js, passing the API key via the env setting as shown in the npx example.

Configuration

The server uses the following environment variables:

GEMINI_API_KEY (Required): Your Google Gemini API key.
GEMINI_MODEL_NAME (Optional): The specific Gemini model to use (e.g., gemini-1.5-flash). Defaults to gemini-2.0-flash. Important: For production or commercial use, ensure you select a model version that is not marked as "Experimental" or "Preview".

Environment variables should be set in the env section of your MCP client's settings file (e.g., mcp_settings.json).

Available Tools

1. `ask_about_youtube_video`

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

Input:
- youtube_url (string, required): The URL of the YouTube video.
- question (string, optional): The specific question to ask about the video. If omitted, a general description is generated.
Output: Text containing the answer or description.

2. `summarize_youtube_video`

Generates a summary of a given YouTube video.

Input:
- youtube_url (string, required): The URL of the YouTube video.
- summary_length (string, optional): Desired summary length ('short', 'medium', 'long'). Defaults to 'medium'.
Output: Text containing the video summary.

3. `extract_key_moments`

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

Input:
- youtube_url (string, required): The URL of the YouTube video.
- number_of_moments (integer, optional): Number of key moments to extract. Defaults to 3.
Output: Text describing the key moments with timestamps.

4. `list_supported_models`

Lists available Gemini models that support the generateContent method (fetched via REST API).

Input: None
Output: Text listing the supported model names.

Important Notes

Model Selection for Production: When using this server for production or commercial purposes, please ensure the selected GEMINI_MODEL_NAME is a stable version suitable for production use. According to the Gemini API Terms of Service, models marked as "Experimental" or "Preview" are not permitted for production deployment.
API Terms of Service: Usage of this server relies on the Google Gemini API. Users are responsible for reviewing and complying with the Google APIs Terms of Service and the Gemini API Additional Terms of Service. Note that data usage policies may differ between free and paid tiers of the Gemini API. Do not submit sensitive or confidential information when using free tiers.
Content Responsibility: The accuracy and appropriateness of content generated via the Gemini API are not guaranteed. Use discretion before relying on or publishing generated content.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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

Name	Required	Description	Default
`youtube_url`	Yes
`question`	No	Question 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

Name	Required	Description	Default
`youtube_url`	Yes
`number_of_moments`	No	Number 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

Name	Required	Description	Default
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

Name	Required	Description	Default
`youtube_url`	Yes
`summary_length`	No	Desired 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. Dates show when Glama detected each change.

4 tool updatesApr 3, 2025
- First observedask_about_youtube_video
- First observedextract_key_moments
- First observedlist_supported_models
- First observedsummarize_youtube_video

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0

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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YouTube Vision MCP Server (youtube-vision)

Features

Prerequisites

Installation & Usage

Installing via Smithery

Option 1: Using npx (Recommended for quick use)

Option 2: Manual Installation (from Source)

Configuration

Available Tools

1. ask_about_youtube_video

2. summarize_youtube_video

3. extract_key_moments

4. list_supported_models

Important Notes

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Appeared in Searches

Latest Blog Posts

MCP directory API

YouTube Vision MCP Server (`youtube-vision`)

1. `ask_about_youtube_video`

2. `summarize_youtube_video`

3. `extract_key_moments`

4. `list_supported_models`