Public TikTok profiles, videos, comments and keyword search as JSON. No developer account.
Scored across 4 tools
Each tool targets a clearly distinct resource: comments, posts, profile, and search. There is no functional overlap or ambiguity between them.
All tools follow the same hasdata_tiktok_<resource>_getTikTok<Resource> pattern, which is predictable. The mix of snake_case and CamelCase is slightly verbose but consistent across the set.
Four tools is well-scoped for a focused TikTok data access server, covering the primary read operations without unnecessary bloat.
The core public TikTok data surface is covered: search, profile, posts, and comments. A direct video-by-ID lookup is missing, but the existing tools handle the main workflows.
Get TikTok Comments
Fetches the comments on a public TikTok video by its numeric video id, or the replies to a specific comment when commentId is given. Each comment returns text, like count, timestamp, reply count, and author (username, nickname, avatar, plus hasdataLink to the profile endpoint and hasdataPostsLink to the posts endpoint). Supports token-based pagination via nextPageToken. Use for sentiment analysis, engagement research, or building comment datasets from a video discovered via the posts or search APIs.
| Name | Required | Description | Default |
|---|---|---|---|
| videoId | Yes | The numeric id of the video (the number after `/video/` in a TikTok URL). | |
| commentId | No | When provided, returns the replies to this comment instead of the video's top-level comments. | |
| nextPageToken | No | Defines the next page token. Use the `nextPageToken` value returned by the previous response. Omit it to fetch the first page. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden and explains read-only fetching of public comments, pagination via nextPageToken, and the returned comment fields. It does not mention rate limits or error behavior, but the disclosed behavior is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, well-organized, and free of filler. Key information about input, output fields, pagination, and use cases is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although there is no output schema, the description enumerates returned comment fields and pagination behavior, which gives an agent enough context for typical use. It lacks response envelope or error details, but those are not essential for selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters have descriptive schema entries, and the description adds practical context: videoId is the number after /video/ in a TikTok URL, commentId selects replies, and nextPageToken uses the previous response value with omission for the first page.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it fetches TikTok comments by video ID or replies by comment ID, distinguishing it from sibling TikTok profile, posts, and search tools. The verb 'Fetches' and the resource 'comments on a public TikTok video' make the purpose specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides concrete use cases such as sentiment analysis, engagement research, and building comment datasets, and notes that videos may be discovered via posts or search APIs. It does not explicitly contrast with sibling tools, but the guidance is sufficient for typical selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get TikTok Posts
Fetches a page of videos for a public TikTok account by username (handle) and returns each video with description, hashtags, mentions, like/comment/share/play/collect counts, cover and playable video URLs, music, duration, and timestamp. Returns a page of ~35 videos plus a nextPageToken; pass that token back to walk the account history one page at a time. Use to monitor competitor content, track engagement of creator videos, or build datasets of account content for vetting and analytics.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | The TikTok username of the account whose videos you want to scrape, with or without the `@` symbol. | |
| nextPageToken | No | Defines the next page token. It is used for retrieving the next page of results. Use the `nextPageToken` value returned by the previous response. Omit it to fetch the first page. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses pagination via nextPageToken, a page size of ~35 videos, and the public-account constraint. It does not mention rate limits or error cases, but the read-only nature is clearly implied by 'Fetches'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense and front-loaded, with the core behavior stated immediately and use cases at the end. The opening line repeats the title, but the rest of the content earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter fetch tool with no output schema, the description covers what is returned, pagination, and typical use cases. It is complete enough for correct invocation, though it omits potential failure modes and rate-limit context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and both parameters are already well-documented in the schema. The description reinforces the handle and pagination concepts but does not add significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a page of videos for a public TikTok account by username, and enumerates the returned fields. This distinguishes it from sibling tools focused on comments, profile, and search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit use cases are given: monitoring competitors, tracking engagement, and building datasets for analytics. It does not name alternative tools or exclusions, but the context is clear enough for selecting this tool over the siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get TikTok Profile
Fetches a public TikTok profile by username (handle) and returns nickname, biography, bio link, avatar URLs, verified/private flags, account creation time, and followers, follows, likes, videos, and friends counts. Use to enrich CRM/lead records, verify influencer reach before outreach, monitor competitor accounts, or build datasets of creator metadata for vetting and analytics.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | The TikTok username of the profile you want to scrape, with or without the `@` symbol. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full behavioral burden. It accurately conveys that this is a read operation (fetches) on public profiles, but it does not disclose potential edge cases like errors for non-existent handles, rate limits, or authentication requirements. For a simple GET it is acceptable but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded with the tool's purpose, and avoids unnecessary fluff. The second sentence adds value by listing return fields and use cases. It is efficiently written without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description covers the essential information: what it does, what it returns, and typical use cases. It does not discuss error handling or output formatting, but given the simplicity of the operation, it is sufficiently complete for an agent to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'handle' is fully documented in the schema with an explanation of format (with or without '@'). The description adds no additional semantics beyond restating the parameter name. Since schema coverage is 100%, the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches a public TikTok profile by username and enumerates the exact data returned (nickname, bio, counts, flags). It is specific about the resource and action, and its distinctness from sibling tools (comments, posts, search) is evident from the resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage contexts (CRM enrichment, influencer vetting, competitor monitoring) which implicitly distinguish it from siblings that handle posts, comments, or search. It does not explicitly state when not to use it or name alternatives, but the context is unambiguous enough for an agent to select it correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search TikTok
Searches TikTok by keyword and returns either videos (with description, hashtags, mentions, like/comment/share/play counts, cover and playable video URLs, music, and author) or users (nickname, bio, avatar, verified flag, follower count). Each author and each user carries a hasdataLink to their profile endpoint and a hasdataPostsLink to their posts endpoint. Supports token-based pagination via nextPageToken. Use for content discovery, trend research, influencer discovery, or building datasets from a keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | What to search for — videos or users. Defaults to video. | |
| keyword | Yes | The phrase to search for on TikTok. | |
| nextPageToken | No | Defines the next page token. Use the `nextPageToken` value returned by the previous response. Omit it to fetch the first page. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should carry the full burden of disclosing behavioral traits. It does not state that the operation is read-only, nor does it mention rate limits, data modification, or other side effects. The absence of such details leaves behavioral transparency incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise and well-structured. It conveys the core functionality and relevant details without unnecessary verbosity, though it could be slightly tighter by removing redundant phrasing like repeating 'hasdataLink' and 'hasdataPostsLink'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description does well to enumerate the return fields (e.g., description, likes, comments, author) and explain pagination. It also gives practical use cases. Minor gaps include not explaining the structure of the nested author/user objects or the exact format of the response envelope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers all parameters with descriptions (100% coverage), and the tool description adds some context about pagination usage. However, it largely repeats the schema information without significantly deepening understanding, so it provides marginal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches TikTok by keyword and returns videos or users, with a list of fields. It also names specific use cases like content discovery and trend research, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides guidance on when to use the tool (content discovery, trend research, etc.) and explains pagination with nextPageToken. However, it does not explicitly contrast with sibling tools, though the distinct focus on search is implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
hasdata_tiktok_comments_getTikTokCommentshasdata_tiktok_posts_getTikTokPostshasdata_tiktok_profile_getTikTokProfilehasdata_tiktok_search_getTikTokSearchTikTok profiles (followers, bio) and per-video stats by handle or URL. No login. Pay per result.
Unofficial TikTok API & scraper: creator analytics, video data, comments, search. x402, no API key.
Get social media data from Instagram and TikTok: profiles, posts, videos, comments, and more.
13 TikTok endpoints. Pay per call in USDC via x402.
Add one secure layer between your agents and this server.
