Provides tools for extracting YouTube video transcripts and metadata, including video descriptions, duration, channel information, and available languages.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@youtube-mcp get the transcript for https://www.youtube.com/watch?v=dQw4w9WgXcQ"
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.
An MCP server for extracting YouTube video transcripts and metadata.
{
"mcpServers": {
"youtube": {
"command": "uvx",
"args": ["kzmshx-youtube-mcp"]
}
}
}Related MCP server: ytt-mcp
If you prefer to install globally:
pip install kzmshx-youtube-mcp
# or
uv tool install kzmshx-youtube-mcpGet transcript text from a YouTube video.
Parameter | Type | Description |
| string | YouTube URL or video ID |
| string | Language code (default: auto) |
| bool | Include timestamps (default: false) |
Example:
// Input
{ "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" }
// Output
{
"video_id": "dQw4w9WgXcQ",
"language": "en",
"transcript": "We're no strangers to love\nYou know the rules and so do I..."
}Get metadata from a YouTube video.
Parameter | Type | Description |
| string | YouTube URL or video ID |
Example:
// Input
{ "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" }
// Output
{
"id": "dQw4w9WgXcQ",
"title": "Rick Astley - Never Gonna Give You Up",
"description": "...",
"duration": 213,
"channel": "Rick Astley",
"upload_date": "2009-10-25",
"view_count": 1500000000,
"available_languages": ["en", "ja", "es", "fr"]
}MIT
Get transcript text from a YouTube video.
Args: url: YouTube URL or video ID language: Language code (default: auto-detect) with_timestamps: Include timestamps in output
Returns: Dict containing video_id, language, and transcript text
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| language | No | auto | |
| with_timestamps | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description explains the return structure (dict with video_id, language, transcript). It does not disclose potential errors, rate limits, or side effects, but for a simple read operation, basic behavioral context is covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a clear structure: a one-line purpose, structured Args, and Returns sections. No redundant information; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (implied), the description adequately explains the return format. It covers the main functionality and parameters, though it could detail error handling or edge cases for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds clear meaning for all three parameters (url, language, with_timestamps) in the 'Args' section, including defaults and purpose. This compensates for the missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get transcript text from a YouTube video,' specifying the verb and resource. It effectively distinguishes from the sibling tool 'get_video_info', which presumably handles video metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, nor does it mention prerequisites or scenarios where it should not be used. It only explains parameters without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get metadata from a YouTube video.
Args: url: YouTube URL or video ID
Returns: Dict containing video metadata
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only mentions it returns a dict of metadata but does not disclose side effects (e.g., network call) or whether it is read-only. Minimal behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Short, front-loaded description with clear Args and Returns sections. Every sentence provides necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite low complexity and presence of output schema, the description lacks any usage context or constraints (e.g., authentication, rate limits). Adequate but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds value by specifying the 'url' parameter can be a URL or video ID, beyond the schema's type-only definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Get metadata from a YouTube video', specifying the verb and resource. Differentiates from sibling 'get_transcript' which serves a different purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 like 'get_transcript'. Missing context on appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_transcriptget_video_infoEach tool has a clearly distinct purpose: one retrieves transcript text, the other fetches video metadata. There is no overlap or ambiguity.
Both tool names follow a consistent 'get_' prefix with noun (transcript, video_info), forming a predictable verb_noun pattern.
With only 2 tools, the server feels undersized for a YouTube integration. While the tools are focused, the count is on the low end for a typical MCP server.
The server covers only two basic reading operations, missing common YouTube actions like searching, listing comments, or managing playlists. Significant gaps exist for a comprehensive YouTube toolset.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
An MCP server that gives any LLM or agent clean YouTube transcripts on demand: a single video, a whole channel, or a playlist, plus AI cleanup of auto-generated captions. API-key auth, credit-based, same backend as the public v1 API. Get a free API key with 25 free credits at youtubetranscriptdownload.com/account.
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