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kdr

yt-mcp-server

by kdr

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    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.

    Naming Consistency5/5

    All tools follow a consistent get_ verb_noun pattern, clearly indicating the returned resource. The naming convention is uniform and predictable.

    Tool Count5/5

    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.

    Completeness5/5

    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.

  • Average 3.7/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    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.

    Conciseness4/5

    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.

    Completeness3/5

    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.

    Parameters4/5

    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.

    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 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.

    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 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.

  • 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. 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.

    Conciseness5/5

    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.

    Completeness3/5

    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.

    Parameters4/5

    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.

    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 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.

    Usage Guidelines3/5

    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.

  • Behavior3/5

    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.

    Conciseness5/5

    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.

    Completeness4/5

    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.

    Parameters4/5

    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.

    Purpose5/5

    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.

    Usage Guidelines3/5

    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.

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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