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yt-mcp

Name: yt-mcp
Author: PakmanGames

by PakmanGames

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Python

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yt-mcp

AI 어시스턴트에게 YouTube 동영상에 대한 깊이 있는 멀티모달 인식 기능을 제공하는 완전 로컬 MCP(Model Context Protocol) 서버입니다. API 키가 필요하지 않습니다. 모든 처리는 yt-dlp, OpenAI Whisper, FFmpeg, PySceneDetect 및 librosa를 통해 기기 내에서 실행됩니다.

참고: 이 저장소에는 Gemini API를 사용하는 실험적인 TypeScript 서버(src/)도 포함되어 있습니다. 해당 서버는 현재 활발히 개발 중이 아니며, Python 로컬 서버(server/)가 주요 구현체입니다.

작동 원리

YouTube URL
    │
    ▼
yt-dlp ──────────────── download video.mp4
    │                   extract audio.wav (16 kHz mono)
    ▼
Whisper ─────────────── timestamped transcript (word-level)
    │
    ▼
PySceneDetect ────────── detect scene-cut timestamps
    │
    ▼
FFmpeg ──────────────── extract keyframe JPEGs at scene cuts
    │
    ▼
OpenCV ──────────────── pixel-diff animation detection
    │
    ▼
librosa ─────────────── energy · tempo · music vs speech
    │
    ▼
timeline.py ─────────── unified JSON timeline (all signals, time-aligned)

모든 결과는 /tmp/yt-analysis-cache/<video_id>/에 캐시됩니다. 동일한 URL을 다시 호출하면 즉시 결과가 반환됩니다.

사전 요구 사항

# macOS
brew install ffmpeg

# Ubuntu / Debian
sudo apt install ffmpeg

# Verify
ffmpeg -version
python3 --version   # must be 3.10+

설치

git clone https://github.com/yourusername/yt-mcp.git
cd yt-mcp

# Create and activate a virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate        # macOS / Linux
# .venv\Scripts\activate         # Windows

pip install -r requirements.txt

Whisper 모델 가중치는 첫 번째 전사 호출 시 자동으로 다운로드됩니다(base의 경우 약 75MB, large의 경우 약 1.5GB).

MCP 통합

MCP 클라이언트는 서버를 하위 프로세스로 생성하며, 셸이나 venv를 자동으로 활성화하지 않습니다. venv의 Python 인터프리터를 절대 경로를 사용하여 직접 지정해야 합니다.

venv를 활성화한 후 인터프리터 경로를 찾으세요:

source .venv/bin/activate
which python   # e.g. /Users/you/repos/yt-mcp/.venv/bin/python

Claude Code:

claude mcp add -s user yt-mcp -- /path/to/yt-mcp/.venv/bin/python /path/to/yt-mcp/server/main.py

Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json에 추가:

{
  "mcpServers": {
    "yt-mcp": {
      "command": "/path/to/yt-mcp/.venv/bin/python",
      "args": ["/path/to/yt-mcp/server/main.py"]
    }
  }
}

/path/to/yt-mcp를 저장소를 복제한 절대 경로로 바꾸세요. Windows의 경우 인터프리터는 .venv\Scripts\python.exe에 있습니다.

도구

`get_video_transcript`

OpenAI Whisper를 사용하여 YouTube 동영상을 전사합니다(완전히 로컬에서 실행).

매개변수	유형	기본값	설명
`youtube_url`	string	—	전체 YouTube URL
`model_size`	string	`base`	`tiny` · `base` · `small` · `medium` · `large`

응답:

{
  "title": "Video Title",
  "duration": 847,
  "language": "en",
  "full_text": "Welcome to this video...",
  "segments": [
    {
      "t_start": 0.0,
      "t_end": 4.5,
      "text": "Welcome to this video.",
      "words": [{ "word": "Welcome", "start": 0.0, "end": 0.6 }]
    }
  ]
}

`get_video_frames`

키프레임을 base64로 인코딩된 JPEG로 추출합니다. 장면 감지를 위해 PySceneDetect를, 추출을 위해 FFmpeg를 사용합니다.

매개변수	유형	기본값	설명
`youtube_url`	string	—	전체 YouTube URL
`strategy`	string	`scene`	`scene` · `interval` · `both`
`interval`	integer	`30`	프레임 간 간격(초) (`interval` 또는 `both` 전략의 경우)

응답:

{
  "title": "Video Title",
  "duration": 847,
  "duration_formatted": "14:07",
  "frame_count": 12,
  "strategy": "scene",
  "frames": [
    {
      "t": 0.0,
      "t_formatted": "0:00",
      "keyframe": "<base64 JPEG>",
      "scene_change": false,
      "animation_detected": false
    }
  ],
  "summary": [ /* same list without keyframe bytes — for quick review */ ]
}

`get_audio_features`

librosa를 사용하여 오디오 특성을 분석합니다(로컬에서 실행).

매개변수	유형	기본값	설명
`youtube_url`	string	—	전체 YouTube URL
`segment_duration`	integer	`30`	분석 창 크기(초)

응답:

{
  "title": "Video Title",
  "duration": 847,
  "segment_duration": 30,
  "segments": [
    {
      "t_start": 0.0,
      "t_end": 30.0,
      "energy": "medium",
      "music": false,
      "tempo_bpm": 95.0,
      "rms_db": -22.1
    }
  ]
}

`get_full_context`

주요 도구. 전사 + 장면 경계 + 애니메이션 감지 + 오디오 특성이 모두 시간 동기화된 완전한 멀티모달 타임라인을 반환합니다.

매개변수	유형	기본값	설명
`youtube_url`	string	—	전체 YouTube URL
`include_frames`	boolean	`false`	세그먼트별 base64 키프레임 포함 여부
`model_size`	string	`base`	Whisper 모델 크기

응답:

{
  "title": "How Transformers Work",
  "channel": "AI Explained",
  "duration": 847,
  "duration_formatted": "14:07",
  "language": "en",
  "description": "In this video...",
  "segments": [
    {
      "t_start": 0.0,
      "t_end": 12.0,
      "transcript": "Welcome to this video on transformers...",
      "keyframe": null,
      "scene_change": false,
      "animation_detected": false,
      "audio": {
        "energy": "low",
        "speech_rate": "normal",
        "music": true,
        "tempo_bpm": 0.0,
        "rms_db": -28.4
      }
    }
  ]
}

컨텍스트 윈도우 팁: 먼저 include_frames=false로 get_full_context를 호출하여 동영상 구조를 파악한 다음, 관심 있는 특정 타임스탬프에 대해 get_video_frames를 호출하세요.

지원되는 URL 형식

https://www.youtube.com/watch?v=VIDEO_ID
https://youtu.be/VIDEO_ID
https://youtube.com/shorts/VIDEO_ID

환경 변수

변수	기본값	설명
`YT_CACHE_DIR`	`/tmp/yt-analysis-cache`	다운로드된 동영상 및 오디오를 위한 캐시 디렉터리

개발

# Activate the venv first
source .venv/bin/activate

# Run the server directly (stdio mode — same as MCP clients use)
python server/main.py

# Quick smoke test
python -c "
from server.utils.downloader import VideoDownloader
from server.tools.transcript import get_transcript
d = VideoDownloader()
vp, ap, info = d.download('https://www.youtube.com/watch?v=jNQXAC9IVRw')
print(get_transcript(ap)['language'])
"

테스트

Python 서버에는 6개 모듈에 걸쳐 164개의 테스트로 구성된 전체 단위 테스트 제품군이 있습니다. 모든 테스트는 네트워크 액세스나 모델 다운로드 없이 실행되며, 모든 외부 종속성(Whisper, librosa, FFmpeg, PySceneDetect, OpenCV, yt-dlp)은 모의 객체(mock)로 처리됩니다.

테스트 종속성 설치

pip install -r requirements-dev.txt

전체 제품군 실행

python -m pytest

예상 출력: 164 passed in ~4s

특정 모듈에 대한 테스트 실행

python -m pytest tests/test_downloader.py   # VideoDownloader + VideoInfo
python -m pytest tests/test_transcript.py   # Whisper wrapper + range helpers
python -m pytest tests/test_frames.py       # FFmpeg, PySceneDetect, OpenCV
python -m pytest tests/test_audio.py        # librosa AudioAnalyzer
python -m pytest tests/test_timeline.py     # build_timeline + speech rate
python -m pytest tests/test_main.py         # all 4 MCP tool handlers

이름으로 단일 테스트 실행

python -m pytest tests/test_timeline.py::TestBuildTimeline::test_rapid_cuts_below_min_merged -v

실제 동영상을 사용한 라이브 스모크 테스트

아래 예시는 プリマドンナ / 星街すいせい(호시마치 스이세이 · Suisei Channel, 2:52)를 사용합니다. 이는 다국어 Whisper 전사, librosa HPSS를 통한 음악 감지, PySceneDetect를 통한 빠른 장면 전환, OpenCV 픽셀 차이를 통한 애니메이션 감지 등 파이프라인의 모든 계층을 활용하는 일본어 뮤직비디오입니다.

from server.utils.downloader import VideoDownloader
from server.tools.transcript import get_transcript
from server.tools.audio import AudioAnalyzer
from server.tools.frames import detect_scene_timestamps

URL = "https://www.youtube.com/watch?v=M1GYqy0tHV0"

d = VideoDownloader()
video_path, audio_path, info = d.download(URL)

print(f"Title:    {info.title}")        # プリマドンナ / 星街すいせい(official)
print(f"Duration: {info.duration:.0f}s")  # 172

transcript = get_transcript(audio_path, model_size="base")
print(f"Language: {transcript['language']}")  # ja

cuts = detect_scene_timestamps(video_path)
print(f"Scene cuts detected: {len(cuts)}")    # typically 30–60 for a music video

analyzer = AudioAnalyzer(audio_path)
seg = analyzer.analyze_segment(0, 30)
print(f"First 30s — energy: {seg['energy']}, music: {seg['music']}")
# energy: 'medium' or 'high', music: True

전체 테스트 가이드(픽스처, 모의 패턴, 새 도구 테스트 작성 방법)는 docs/testing.md를 참조하세요.

아키텍처

시스템 설계, 데이터 흐름 및 새 도구 추가 방법에 대한 자세한 설명은 다음을 참조하세요:

docs/architecture.md — 파이프라인 다이어그램 및 주요 설계 결정
docs/python-server.md — 모든 모듈에 대한 구성 요소 참조
docs/extending.md — 새 도구 추가 방법
docs/testing.md — 테스트 제품군 구조, 픽스처 및 새 테스트 작성 방법

TypeScript 서버 (보관됨)

src/ 디렉터리에는 동영상 분석을 Gemini API에 위임하는 실험적인 TypeScript 서버가 포함되어 있습니다. 이 서버는 현재 활발히 개발 중이 아니며 참조용으로만 유지됩니다.

빠른 클라우드 기반 동영상 Q&A를 찾고 있다면, TypeScript 서버의 접근 방식(YouTube URL을 Gemini에 직접 전달)이 빠른 프로토타입에는 효과적이지만, 지속적인 유지 관리가 이루어지는 구현체는 Python 서버뿐입니다.

API 참조는 docs/typescript-server.md를 참조하세요.

라이선스

MIT

Available Tools

5 tools

ask_about_videoB

Ask a specific question about a YouTube video's content. Returns an answer based on the video.

ParametersJSON Schema

Name	Required	Description	Default
`youtube_url`	Yes	Full YouTube URL (youtube.com/watch?v=ID, youtu.be/ID, or youtube.com/shorts/ID)
`question`	Yes	Your question about the video content

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description carries the burden but only states it 'Returns an answer based on the video'. It does not disclose any behavioral traits such as rate limits, authentication needs, or what type of questions are supported. The statement is minimal but not misleading.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that conveys the core purpose efficiently. No wasted words, though it could include more detail without becoming verbose.

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?

For a tool with 2 parameters and no output schema, the description provides a minimal but functional explanation. It lacks details about the format of the answer or limitations, which might be needed for effective use. However, it is not incomplete to the point of being unusable.

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 both parameters adequately. The description adds no new meaning beyond the schema, so a baseline of 3 is appropriate.

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?

Describes the tool as asking a specific question about a YouTube video and returning an answer. The verb 'ask' and resource 'video content' are clear. However, it does not explicitly differentiate from sibling tools like 'summarize_video', which also deals with video content, though the 'specific question' aspect provides some distinction.

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?

Implies usage when needing to ask a question about video content, but does not provide explicit guidance on when to use this tool versus alternatives like 'summarize_video' or when not to use it. No exclusions or prerequisites are mentioned.

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

extract_framesA

Extract frames from a YouTube video at specific timestamps you provide. Use this when you already know the exact timestamps you want (e.g., from get_video_timestamps or video summary).

ParametersJSON Schema

Name	Required	Description	Default
`youtube_url`	Yes	Full YouTube URL (youtube.com/watch?v=ID, youtu.be/ID, or youtube.com/shorts/ID)
`timestamps`	Yes	Array of timestamps in seconds to extract frames from (e.g., [5, 30, 60, 120])
`output_dir`	No	Optional directory to save screenshots. If not provided, uses SCREENSHOT_OUTPUT_DIR env var or temp directory.
`resolution`	No	Output resolution: thumbnail (160p), small (360p), medium (720p), large (1080p), full (original). Default: large	large

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It focuses on the extraction function but doesn't detail behavior like file output format, naming conventions, or error handling. The description adds some context beyond schema but is not comprehensive.

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 two sentences long, directly stating the purpose and usage context with no fluff. It is concise and front-loaded with the key action and resource.

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 moderate complexity (4 parameters with 2 required) and no output schema, the description is adequate but not exhaustive. It covers the primary use case but lacks details on output behavior, which is acceptable since there is no output schema to complement.

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 baseline is 3. The description adds value by clarifying the usage context (timestamps from other tools) but does not provide additional semantic meaning beyond what the schema already offers for each parameter.

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 extracts frames from a YouTube video at specific timestamps. It specifies the resource (YouTube video) and the action (extract frames at timestamps), and distinguishes it from siblings like extract_screenshots by focusing on user-provided timestamps.

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

Usage Guidelines5/5

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

The description explicitly says when to use this tool: when you already know exact timestamps. It suggests examples like using timestamps from get_video_timestamps or video summary, implying when not to use it (if you don't have timestamps) and providing context for alternatives.

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

extract_screenshotsA

Extract key screenshots from a YouTube video at important moments. Uses AI to identify visually significant timestamps, then extracts frames. Returns both base64 images and optionally saves to disk.

ParametersJSON Schema

Name	Required	Description	Default
`youtube_url`	Yes	Full YouTube URL (youtube.com/watch?v=ID, youtu.be/ID, or youtube.com/shorts/ID)
`count`	No	Number of screenshots to extract (1-20, default: 5)
`output_dir`	No	Optional directory to save screenshots. If not provided, uses SCREENSHOT_OUTPUT_DIR env var or temp directory.
`focus`	No	Optional focus for timestamp selection (e.g., 'product demos', 'code examples', 'diagrams'). Default analyzes for general key moments.
`resolution`	No	Output resolution: thumbnail (160p), small (360p), medium (720p), large (1080p), full (original). Default: large	large

TDQS

A3.7/5.0

Behavior4/5

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

Discloses that it uses AI for timestamp selection, returns base64 images, and optionally saves to disk. With no annotations provided, this description carries the burden well. However, more details about performance or API calls could enhance transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each providing distinct value: purpose, method, and output. Could be more concise by removing 'Optionally saves to disk' since it's covered in output_dir parameter.

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

Completeness4/5

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

Covers main aspects: video input, AI selection, number of screenshots, output format, and save behavior. However, no output schema means description could mention return format (array of base64 strings). Overall adequate.

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 baseline is 3. Description adds context for 'focus' parameter but doesn't elaborate on 'resolution' enum meaning beyond schema. The 'output_dir' description adds context about fallback behavior, which adds value.

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?

Clearly states it extracts screenshots from YouTube videos at important moments using AI. Distinguishes from siblings like 'extract_frames' by mentioning AI to find visually significant timestamps, but could be more specific about difference from 'extract_frames'.

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?

Implies use for extracting key screenshots, but no explicit guidance on when to use vs siblings like 'extract_frames' or 'get_video_timestamps'. No mention of prerequisites (e.g., need ffmpeg).

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

get_video_timestampsA

Preview mode: Use AI to identify important moments in a YouTube video and return their timestamps WITHOUT extracting frames. Use this to preview what timestamps would be selected before committing to extraction.

ParametersJSON Schema

Name	Required	Description
`youtube_url`	Yes	Full YouTube URL (youtube.com/watch?v=ID, youtu.be/ID, or youtube.com/shorts/ID)
`count`	No	Number of timestamps to identify (1-20, default: 5)
`focus`	No	Optional focus for timestamp selection (e.g., 'product demos', 'code examples', 'diagrams'). Default analyzes for general key moments.

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It clearly states that it uses AI to identify timestamps and does not extract frames. However, it does not disclose other behavioral traits such as API rate limits, how the AI works, or constraints on video length/format.

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 concise, consisting of two sentences. The first sentence clearly defines the tool's function, and the second sentence explains its purpose relative to other tools. No unnecessary information is present.

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

Completeness4/5

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

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is fairly complete. It explains the tool's purpose and its relationship to extraction tools. However, it does not mention return format or behavior for invalid URLs, but the schema provides validation hints.

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 schema already provides descriptions for all parameters (100% coverage), so the description adds minimal value beyond the schema. However, the description does give context for the 'focus' parameter by providing examples like 'product demos' and 'code examples', which adds nuance beyond the schema's generic description.

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 purpose: to use AI to identify important moments in a YouTube video and return their timestamps without extracting frames. It distinguishes itself from sibling tools by emphasizing that it is a preview mode that does not extract frames, which sets it apart from extract_frames and extract_screenshots.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: 'to preview what timestamps would be selected before committing to extraction.' However, it does not explicitly mention when not to use it or suggest alternatives, but the sibling list makes it clear that extraction tools exist for different purposes.

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

summarize_videoA

Summarize a YouTube video's content. Returns a text summary based on the specified detail level.

ParametersJSON Schema

Name	Required	Description	Default
`youtube_url`	Yes	Full YouTube URL (youtube.com/watch?v=ID, youtu.be/ID, or youtube.com/shorts/ID)
`detail_level`	No	Level of detail: brief (2-3 sentences), medium (key points with timestamps), detailed (comprehensive breakdown)	medium

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description must cover behavioral aspects. It mentions it returns a text summary based on detail level, but does not disclose processing time, input validation, or error handling. The behavior is straightforward but minimally described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: first states the core function, second explains output based on parameter. It is concise but could be more front-loaded about what the user gets. No fluff, but slightly vague on output format.

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 output schema, the description should explain return format. It says 'returns a text summary' but not whether it's plain text, structured, or includes timestamps. With siblings like ask_about_video, more detail on output would help. Scores 3 as it covers basic purpose but lacks output specificity.

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 100%, so baseline is 3. The description adds value by explaining the detail_level enum values (brief, medium, detailed) with concrete expectations like 'key points with timestamps', which is not in the schema. This extra context earns a 4.

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 it summarizes a YouTube video's content and returns a text summary based on a detail level. It distinguishes from siblings like ask_about_video (querying) and extract_frames (visual extraction), but could be more explicit about the summary format.

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?

No explicit when-to-use or alternatives are provided. The description implies usage when a text summary is needed, but does not clarify when to choose this over ask_about_video or other tools. However, the enum options for detail_level give some usage guidance.

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.

5 tool updatesv0.1.0Apr 24, 2026
- First observedask_about_video
- First observedextract_frames
- First observedextract_screenshots
- First observedget_video_timestamps
- First observedsummarize_video

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.8/5.0

Scored 2026-04-24 01:40 across 5 tools

Disambiguation4/5

Tools are mostly distinct: ask_about_video and summarize_video both deal with content analysis but differ in question vs. summary. extract_frames and extract_screenshots have clear differences (given timestamps vs. AI-chosen moments). get_video_timestamps is a preview for extraction, reducing overlap. Minor confusion possible between ask_about_video and summarize_video.

Naming Consistency4/5

All tool names use verb_* pattern with snake_case (ask_about_video, extract_frames, etc.), which is consistent. Some verbs are compound (ask_about), but the pattern is predictable and clear.

Tool Count5/5

5 tools is appropriate for a YouTube MCP: they cover core operations (summarize, Q&A, frame extraction, screenshot extraction, timestamp preview). Not too few or too many.

Completeness4/5

The tool set covers main user needs: summarization, Q&A, and visual extraction with a preview step. Missing operations like searching videos or managing playlists, but those may be out of scope. The workflow from preview to extraction is well-supported.

Maintenance

ActivityInactive

ResponsivenessSlow

Resources

Need Help?

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yt-mcp

목차

작동 원리

사전 요구 사항

설치

MCP 통합

도구

get_video_transcript

get_video_frames

get_audio_features

get_full_context

지원되는 URL 형식

환경 변수

개발

테스트

테스트 종속성 설치

전체 제품군 실행

특정 모듈에 대한 테스트 실행

이름으로 단일 테스트 실행

실제 동영상을 사용한 라이브 스모크 테스트

아키텍처

TypeScript 서버 (보관됨)

라이선스

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

`get_video_transcript`

`get_video_frames`

`get_audio_features`

`get_full_context`