Skip to main content
Glama
bartivs

yt-media-info-mcp

by bartivs

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a distinct purpose: metadata extraction, transcript retrieval, and media search. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (extract_info, get_transcript, search_media) using lowercase snake_case.

    Tool Count5/5

    Three tools is appropriate for a focused media info server, covering core operations without excess.

    Completeness5/5

    The tool set covers the main use cases for media information: metadata extraction, transcript fetching, and search. No obvious gaps for its stated purpose.

  • Average 3.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 22 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under MIT License.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions optional timestamps but does not describe output format, error handling, rate limits, authentication requirements (despite username/password params), or any side effects. The behavior is only superficially described.

    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 sentence with no wasted words. It is front-loaded with the core action ('Fetch subtitles or transcript text') and mentions the key optional feature. Every word serves a purpose.

    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 absence of annotations and output schema, and the presence of authentication-related parameters (username, password), the description should provide more context on return values, error scenarios, and authentication flow. As is, it leaves significant gaps for a non-trivial tool with 5 parameters.

    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 the schema already documents all parameters adequately. The description adds 'optionally with timestamp segments' which corresponds to the timestamps parameter but does not enhance schema information. Baseline score of 3 is appropriate.

    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?

    Description clearly states the tool fetches subtitles or transcript text from a media URL with optional timestamps. It uses a specific verb ('Fetch') and resource ('subtitles or transcript text'), and distinguishes itself from siblings like 'extract_info' (metadata extraction) and 'search_media' (search).

    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?

    No explicit guidance on when to use this tool versus alternatives. The description does not mention when not to use it, nor does it reference sibling tools or provide context for preferred usage scenarios. The agent must infer use from the description alone.

    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?

    No annotations provided, so the description carries the full burden. It implies a read operation ('Extract') and lists metadata fields, but does not disclose authentication requirements, rate limits, error handling, or limitations. It mentions 'and more' vaguely.

    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?

    Single sentence, front-loaded with the operation, and includes a representative list. No unnecessary words or repetition.

    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 4 parameters, no output schema, no annotations, and two siblings, the description gives a good overview but lacks usage context, return format details, and potential constraints (e.g., auth, rate limits). Adequate but incomplete.

    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 each parameter already has a description. The tool description adds an overview of the extracted metadata but does not elaborate on parameter usage beyond the schema. Baseline of 3 is appropriate.

    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 action ('Extract') and specific resource ('rich metadata from a media URL'), including a list of extracted items (title, description, duration, etc.), which distinguishes it from siblings get_transcript and search_media.

    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?

    No guidance on when to use this tool versus alternatives. No mention of prerequisites, context, or exclusions. Agent must infer usage from the description alone.

    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?

    No annotations are provided, so the description must disclose behavioral traits. It states the tool uses yt-dlp search, which suggests a read-only operation, but does not explicitly confirm it is non-destructive or mention any side effects, permission requirements, or rate limits. Adequate but could be more explicit.

    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?

    Two concise sentences that immediately convey the tool's purpose, scope, and supplementary nature. No redundant words; every sentence adds value.

    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?

    The description explains the tool's purpose and method, but lacks details about output structure or how results relate to sibling tools (extract_info, get_transcript). Without an output schema, the description should guide the agent on what to expect from results. Adequate but not fully complete.

    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% with each parameter fully described. The tool description adds context about yt-dlp and platform mapping (e.g., 'youtube → ytsearch:'), but the schema already covers the enum values. Thus, the description adds marginal value beyond the schema, meeting the baseline.

    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 explicitly states the verb 'Search' and resource 'media', specifies supported platforms (YouTube, etc.) and the underlying technology (yt-dlp), and clarifies its role as a supplementary discovery tool, which clearly distinguishes it from siblings like extract_info and get_transcript.

    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 mentions it is a 'supplementary discovery tool meant to complement web search', implying when to use it for media discovery, but does not explicitly state when not to use it or name alternatives. It provides implied context but lacks clear exclusions.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

yt-media-info-mcp MCP server

Copy to your README.md:

Score Badge

yt-media-info-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bartivs/yt-media-info-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server