Provides tools for working with YouTube URLs, video IDs, and thumbnails.
Click on "Deploy 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., "@yt-mcp-server normalize this YouTube URL: https://youtu.be/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.
MCP server with various utility functions for dealing with YouTube data. This server provides tools for working with YouTube URLs, video IDs, and thumbnails.
Related MCP server: YouTube MCP Server
Python 3.12 or higher
UV package manager
If you haven't installed UV yet, you can install it using:
brew install uv
# alternatively curl -LsSf https://astral.sh/uv/install.sh | shThis is the simplest way to install and run the server:
uvx --from git+https://github.com/kdr/yt-mcp-server.git serverIf you want to modify the code locally:
git clone https://github.com/kdr/yt-mcp-server.git
cd yt-mcp-server
uv venv
source .venv/bin/activate # On Unix/macOS
# or
.venv\Scripts\activate # On Windows
uv pip install -e .Add the following configuration to your MCP client settings:
{
"mcpServers": {
"yt-mcp-server": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/kdr/yt-mcp-server.git",
"server"
]
}
}
}For local development (Method 2), use this configuration instead:
{
"mcpServers": {
"yt-mcp-server": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/PARENT/FOLDER/yt-mcp-server/yt_mcp_server",
"run",
"main.py"
]
}
}
}The following tools are available to the LLM:
get_watch_url: Returns the YouTube watch URL for a given video ID, optionally starting at a specific time
Parameters:
video_id: The YouTube video ID
start_time: (Optional) The start time in seconds
get_thumbnail_url: Returns the thumbnail URL for a given YouTube video ID
Parameters:
video_id: The YouTube video ID
get_normalized_url: Normalizes various YouTube URL formats to the canonical watch URL and extracts the video ID
Parameters:
url: The YouTube URL to normalize
Returns the normalized YouTube URL for a given URL.
Args:
url: The YouTube URL to normalize.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
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 states the action ('returns normalized URL') without disclosing edge cases, error handling, side effects, or the meaning of 'normalized.' For a simple tool this is minimally transparent, but significant behavioral context is missing.
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 very concise: one sentence plus an Args block. It is front-loaded with the main purpose and contains no redundant or irrelevant information. Every element earns its place.
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?
For a single-parameter, no-annotation, no-output-schema tool, the description covers the basic function and parameter meaning. However, it does not explain what 'normalized' entails, the exact return format, or how this differs from sibling tools. This leaves some contextual gaps for an agent deciding between tools.
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?
The schema provides no description for the 'url' parameter (0% coverage), but the description's Args section explains it as 'The YouTube URL to normalize.' This adds meaningful semantics beyond the raw string type, adequately compensating for the lack of schema documentation.
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 the tool's function: 'Returns the normalized YouTube URL for a given URL.' This uses a specific verb (returns) and resource (normalized YouTube URL), and the purpose is distinct from sibling tools like get_watch_url and get_thumbnail_url, which return different URL forms.
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 implies usage: when given a YouTube URL, this tool normalizes it. However, it does not explicitly state when to use this tool over the sibling tools, nor does it mention any exclusions or alternatives. The usage context is clear but not elaborated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the thumbnail URL for a given YouTube video ID.
Args:
video_id: The YouTube video ID.
| Name | Required | Description | Default |
|---|---|---|---|
| video_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does not mention whether the URL is always valid, whether any network request occurs, or what happens for invalid video IDs. This lack of detail leaves the agent uncertain about edge cases.
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 single declarative sentence and an Args block that adds semantic context. It is well-structured, though the Args block partially duplicates the schema.
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?
For a one-parameter utility, the description covers the basic purpose and parameter meaning. However, it omits any details about the return value's format or possible failure modes, which is significant given the absence of an output schema and annotations.
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?
The description explicitly labels video_id as 'The YouTube video ID', which adds semantic meaning beyond the schema's plain string type. This clarifies the expected input, though it lacks format constraints like typical ID length.
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 the tool's function: it returns the thumbnail URL for a YouTube video ID. This is a specific verb+resource and distinguishes it from sibling tools like get_watch_url and get_normalized_url.
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 the sibling URL utilities. It simply states the operation without context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the YouTube watch URL for a given video ID, optionally starting at a specific time.
Args:
video_id: The YouTube video ID.
start_time: The start time in seconds
| Name | Required | Description | Default |
|---|---|---|---|
| video_id | Yes | ||
| start_time | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It states it returns a URL and optionally includes a start time, but it does not clarify the exact URL format, whether video_id is validated, or how start_time is encoded. No contradictory claims are present.
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 compact and front-loaded with a clear one-sentence purpose followed by an Args list. Every line provides necessary information, with no filler or 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?
For a simple utility with two parameters and no output schema, the description covers the primary purpose and both parameters adequately. It does not detail the return type or URL structure, but this is not critical given the tool's simplicity. The lack of sibling differentiation is a minor completeness gap.
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 coverage is 0%, so the description must compensate. It does so by explaining video_id as 'The YouTube video ID' and start_time as 'The start time in seconds', adding units and meaning beyond the bare parameter names. However, it does not specify constraints like non-negative start_time or how the time appears in the URL.
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 uses a specific verb ('Returns') and resource ('YouTube watch URL'), clearly distinguishing it from sibling tools like get_thumbnail_url and get_normalized_url. It also mentions the optional start_time parameter, making the tool's purpose unambiguous.
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 implies usage when a watch URL is needed and mentions the optional start time, but it does not explicitly state when to use this tool versus alternatives or provide any exclusion criteria. The presence of sibling tools (thumbnail/normalized) makes the lack of comparison a minor gap.
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.
v0.1.0get_normalized_urlget_thumbnail_urlget_watch_urlScored across 3 tools
Each tool targets a distinct output: watch URL, thumbnail URL, and normalized URL. Inputs differ (video ID vs. URL), so there is no overlap or ambiguity.
All tools follow a consistent get_ verb_noun pattern, clearly indicating the returned resource. The naming convention is uniform and predictable.
The server is narrowly scoped to YouTube URL helpers, and 3 tools is an appropriate size for this purpose. Each tool earns its place without redundancy.
The server covers the core operations for URL generation, thumbnails, and normalization. The get_normalized_url tool effectively handles URL parsing and standardization, leaving no obvious missing functionality.
YouTube discovery, transcripts, library search, and monitors with API keys or OAuth.
YouTube public video, comment, reply, channel, search, and speech-to-text transcript tools.
YouTube video metadata and channel data by URL or @handle. No API key or quota. Pay per result.
The all-in-one YouTube content engine: research, ideas, thumbnails, scripts, and video.
