TikTok MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: one checks for available subtitles, one retrieves post details, and one fetches a specific subtitle. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the need.
Naming Consistency5/5All tools follow a consistent 'tiktok_verb_noun' pattern (tiktok_available_subtitles, tiktok_get_post_details, tiktok_get_subtitle). This predictability enhances usability and reduces confusion.
Tool Count3/5With only 3 tools, the server feels thin for a TikTok integration, as it lacks common operations like searching for videos, uploading content, or managing user interactions. However, the tools cover specific use cases adequately.
Completeness2/5The toolset is severely incomplete for a TikTok server, missing essential CRUD operations such as creating posts, liking videos, or searching content. It only handles retrieval of subtitles and post details, leaving significant gaps in functionality.
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
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- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
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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. The description fails to clarify whether the operation is read-only, requires authentication, or any rate limits. It implies returning subtitle data but doesn't disclose the full behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose and repetitive, restating the same idea multiple times. It could be condensed into a single clear sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should clearly explain the return value structure. It only mentions 'different languages and formats' but lacks specificity, leaving ambiguity about the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for 'tiktok_url'. The tool description adds no additional semantic value beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states it 'Looks up the available subtitle' but also says 'Returns the available subtitle', creating ambiguity about whether it checks availability or retrieves content. With sibling 'tiktok_get_subtitle', the exact purpose is unclear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'looking up if there is any content available' but does not explicitly contrast with siblings or specify when to use this tool instead of alternatives.
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 provided, the description carries the full burden. It discloses the default behavior (returns automatic speech recognition subtitle when no language code is provided) but does not mention error handling, response format details, or performance characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat repetitive ('Get the subtitle ... This is used for getting the subtitle, content or context') and contains a typo ('AVAILABLE_SUBTITLES'). It could be more concise and better formatted with proper spacing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 2 parameters and no output schema or annotations, the description covers basic behavior and default. However, it omits details about the output format and any limitations, leaving some uncertainty about the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by specifying that the language code should come from the 'AVAILABLE_SUBTITLES' tool and explains the default when omitted. This gives meaningful context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool gets the subtitle for a TikTok video URL, mentioning input and optional language code. It references the sibling tool 'AVAILABLE_SUBTITLES' (likely tiktok_available_subtitles) for obtaining language codes, which helps differentiate from siblings like tiktok_get_post_details. However, it could be more explicit about the exact output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a workflow by mentioning the language code from 'AVAILABLE_SUBTITLES', but does not explicitly state when to use this tool vs alternatives. It lacks clear guidance on prerequisites or exclusion conditions.
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 carries the full burden. It correctly implies a read operation and lists outputs, but does not disclose potential side effects, authentication requirements, rate limits, or error handling (e.g., for private videos). The transparency is adequate but could be improved.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat redundant: 'Get the details of a TikTok post.This is used for getting the details of a TikTok post.' It could be more concise by removing the repetition. The list of return fields is clear but unstructured. Overall, it is not excessively long but could be tighter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the core purpose, input format, and key output fields. It lacks information about accessibility restrictions (public only) and error scenarios, but these are minor gaps. The description is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the 'tiktok_url' parameter with examples, and the tool description merely repeats 'Supports TikTok video url as input' without adding new constraints or formatting details. With 100% schema coverage, the description does not significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get the details of a TikTok post.' It specifies the input (TikTok video URL) and lists the specific return fields (description, creator username, hashtags, counts, dates, duration). This differentiates it from sibling tools like tiktok_available_subtitles, which deal with subtitles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description only says 'Supports TikTok video url as input' but does not explicitly compare with sibling tools or state when to use this tool versus alternatives. While the sibling tools have different purposes (subtitles), the lack of explicit guidance reduces the score.
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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