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Seym0n

TikTok MCP

by Seym0n
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.2.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get post details, get subtitles, and search. No overlap, descriptions are clear and specific.

    Naming Consistency5/5

    All tools follow a consistent 'tiktok_verb_noun' pattern (get_post_details, get_subtitle, search). Naming is uniform and predictable.

    Tool Count4/5

    3 tools is appropriate for a focused TikTok MCP that provides core reading operations. Could be slightly expanded, but not under-scoped.

    Completeness3/5

    Covers search, post details, and subtitles, but missing user profiles, trending, or interaction features. Notable gaps for a comprehensive TikTok tool set.

  • 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
    • 1 commit in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries full burden. It lists many return fields (likes, shares, comments, etc.), which is helpful, but does not disclose authentication, rate limits, or potential errors.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is moderately concise but has redundancy (first two sentences say similar things). It lists return fields in a bullet-like manner, which aids readability.

    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 the tool has one parameter and no output schema, the description adequately covers input and output fields. However, it lacks context on error handling, authentication, or pagination if applicable.

    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 already describes the parameter in detail, including URL formats and video ID example. The description adds minimal extra value; it mentions 'supports TikTok video url (or video ID)' which matches the schema.

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

    Purpose4/5

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

    The description clearly states it gets post details and lists specific returned fields, distinguishing it from sibling tools like tiktok_get_subtitle and tiktok_search. However, the first two sentences are redundant.

    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?

    It implies use when post details are needed and specifies input formats (URL or video ID), but does not provide explicit when-to-use or when-not-to-use guidance compared to 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 provided, so description must fully convey behavioral traits. Only states that it searches and returns results, but omits necessary details like read-only nature, authentication, rate limits, or side effects.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two sentences but includes a lengthy list of return fields, making it slightly verbose. Purpose is front-loaded, but structure could be tighter.

    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?

    No output schema, yet description thoroughly lists returned fields and pagination metadata. Adequate for a search tool, though structure of return could be clarified.

    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% and schema descriptions are adequate. Description reiterates parameter usage without adding significant new meaning beyond stating pagination.

    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 'Search' and resource 'TikTok videos', and differentiates from siblings (get_post_details, get_subtitle) by being a general search tool.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Describes when to use (search by keywords, hashtags) and mentions pagination with cursor and search_uid, but does not explicitly compare to alternatives or state when not to use.

    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; it mentions automatic speech recognition fallback but omits other behaviors like rate limits, authorization, or error handling for missing subtitles.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description has some redundancy (e.g., 'Get the subtitle...' and 'This is used for getting...'). Could be more concise without losing clarity.

    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?

    Adequate for a simple tool with two parameters, but lacks details on return format, error handling, and authentication requirements. Not thoroughly 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%, so parameters are already described. The description adds little beyond the schema, only mentioning the automatic speech recognition fallback for missing language code.

    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 gets subtitles for TikTok videos using a URL or ID. This distinguishes it from siblings like tiktok_get_post_details and tiktok_search.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explains the tool is for getting subtitles and mentions optional language code. However, it does not explicitly state when not to use it or compare with alternatives.

    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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