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yangbuyiya

Short Video MCP Server

by yangbuyiya
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.2

  • Disambiguation5/5

    Each tool targets a distinct input method (text, URL, source+ID) for parsing short video information, with clear descriptions preventing overlap.

    Naming Consistency5/5

    All tools follow the consistent pattern of 'input_parse_tool_wrapper', making the naming predictable and easy to understand.

    Tool Count5/5

    Three tools is well-scoped for a server focused on parsing short video references from common inputs, with no unnecessary or missing tools.

    Completeness5/5

    The tool surface covers all common ways to reference a short video (shared text, URL, or direct ID), making it complete for its intended purpose.

  • Average 3.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

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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 provided, so description should fully disclose behavior. Mentions apikey requirement but apikey is not in input schema, causing confusion. No mention of side effects, return format, or error handling.

    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, efficient. The first sentence is a warning but somewhat redundant with parameter list. Could be more structured.

    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?

    Output schema exists but description fails to explain what 'extract video content' means in practice. Missing critical info about how to supply apikey and what the tool returns. Incomplete given the tool's complexity.

    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?

    Adds context to all three parameters (text is Douyin share text with link, defaults for api_base_url and model). However, misses the apikey parameter which description says is required, so incomplete.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Description states 'extract video content' but tool name is 'parse', creating ambiguity. Does not distinguish from sibling tools 'share_url_parse_tool_wrapper' and 'video_id_parse_tool_wrapper'.

    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 siblings. Mentions need for apikey but does not explain prerequisites or alternatives.

    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 must convey behavioral traits. It states that the tool parses a URL and returns code, msg, and data, implying a read-only operation, but does not disclose side effects, authentication needs, or any potential errors. The description lacks sufficient behavioral context.

    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 brief and structured with a purpose statement, parameter list, and return fields. It is appropriately sized for a simple tool, though it could be more front-loaded with the purpose.

    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 one parameter and an output schema, the description covers the core purpose and return structure (code, msg, data). However, it lacks usage guidance, behavioral detail, and any differentiation from siblings, leaving the agent with an incomplete picture for correct invocation.

    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?

    The schema has 0% description coverage (only type string), but the description adds meaning by labeling 'url' as a 'video share link'. However, it does not provide format, constraints, or examples, so it adds minimal value beyond the schema.

    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: 'parse video share link, get video info'. It specifies both the verb (parse) and the resource (video share link), and the sibling tools (share_text_parse, video_id_parse) are distinct, indicating this tool is specifically for URL parsing.

    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 its siblings (share_text_parse_tool_wrapper, video_id_parse_tool_wrapper). There is no mention of prerequisites, contexts, or exclusions, leaving the agent without direction for tool selection.

    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 provided. Description does not disclose behavioral traits like side effects, authentication, or network usage. Merely states parsing, but fails to clarify if it's a local or API call.

    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?

    Concise: one sentence plus bullet lists of parameters and returns. No redundancy, but bullet lists are not integrated into prose. Front-loaded with action.

    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 2-parameter parse tool with an output schema, the description covers what, parameters, and returns. Lacks examples or error handling, but adequate for low complexity.

    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 0%, but description briefly explains 'source: 视频来源' and 'video_id: 视频ID', adding minimal meaning. Insufficient detail on parameter formats or constraints.

    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 verb '解析' (parse) and resource '视频信息' (video information) based on source and ID. It distinguishes from sibling tools 'share_text_parse_tool_wrapper' and 'share_url_parse_tool_wrapper' by specifying video ID parsing.

    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 when-not-to-use guidance. The tool name and sibling list imply use for video ID parsing, but no context for alternatives or prerequisites is given.

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

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