Search TikTok users by keyword. Returns username, bio, follower/following counts, likes, and video count.
| Name | Required | Description | Default |
|---|---|---|---|
| pages | No | Number of pages to fetch, 1-10 (default: 1) | |
| query | Yes | Search keyword (max 500 characters) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full behavioral burden. It discloses the return fields and the search-by-keyword behavior, which is useful. However, it does not mention pagination behavior (though the pages parameter exists in schema), rate limits, or authentication requirements. The read-only nature is implied by 'search' but not explicitly stated.
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 consists of two short sentences that front-load the purpose and return information. There is no redundant or extraneous content, making it highly concise and well-structured.
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 low-complexity search tool with 2 parameters (1 required), no output schema, and no annotations, the description covers the core purpose and return values. It is missing some minor details like pagination behavior and potential rate limits, but these are partially addressed by the schema (pages parameter). Overall, it is sufficiently complete for an agent to select and invoke the tool.
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% for both query and pages parameters, so the schema fully documents them. The tool description does not add any parameter-specific meaning beyond what the schema already provides, such as syntax or formatting. Baseline 3 is appropriate.
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 'Search TikTok users by keyword' with a specific verb and resource, and lists the return fields (username, bio, follower/following counts, likes, video count). This distinguishes it from sibling tools like search_tiktok (which likely searches videos) and tiktok_user_profile (which retrieves a single profile).
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 implies usage for keyword-based discovery of TikTok users but does not explicitly state when to use this tool versus alternatives such as search_tiktok or tiktok_user_profile. There is no mention of exclusions or preferred scenarios, so guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.
The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.
74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.
The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.
