Influship

List TikTok profile videos

list_tiktok_profile_videos

Read-only

Fetch one cursor-paginated page of current TikTok videos for a username.

Use the returned cursor to paginate and choose latest or popular ordering. Media URLs are temporary. Content always resolves against the US region. Each successful page, including an empty page, costs 0.2 account credits. A private account returns a forbidden error rather than an empty page; do not retry it.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "username": "<username>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "list_tiktok_profile_videos"
  }
}

Input Schema

Name	Required	Description	Default
`cursor`	No
`region`	No		US
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`sort_by`	No		latest
`username`	Yes
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Output Schema

Name	Required	Description	Default
`ok`	No
`data`	No
`count`	No
`results`	No
`has_more`	No
`not_found`	No
`next_cursor`	No
`suggested_followups`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedNov 5, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "username"
-]New value: +[
+  "username",
+  "context",
+  "llm_model"
+]

Changed2 schema fields changedOct 23, 2026
- removedOutput schema / properties / next_cursor / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / next_cursor / type
```
Added value: +[
+  "string",
+  "null"
+]
```
Changed1 schema field changedSep 8, 2026
- changedInput schema / properties / cursor / maxLength
```
Previous value: -512New value: +2048
```
Changed5 schema fields changedAug 28, 2026
- addedInput schema / properties / region / const
```
Added value: +"US"
```
- addedInput schema / properties / region / default
```
Added value: +"US"
```
- removedInput schema / properties / region / maxLength
```
Removed value: -2
```
- removedInput schema / properties / region / minLength
```
Removed value: -2
```
- removedInput schema / properties / region / pattern
```
Removed value: -"^[a-zA-Z]{2}$"
```
AddedAug 12, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Annotations already cover the read-only/open-world/non-destructive profile, and the description adds substantial behavior beyond them: temporary media URLs, mandatory US region resolution, a per-page credit cost including empty pages, and a forbidden-error (non-empty) response for private accounts with explicit 'do not retry' instruction. This is exactly the operational context an agent cannot get from structured fields.

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

Conciseness5/5

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

Five short sentences, front-loaded with the core action, then pagination/ordering, then the constraints that affect correctness (temporary URLs, region, cost, error handling). No filler and no repetition of schema content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return shape need not be described; what remains for a billed, region-locked, paginated listing tool is cost, region, error and retry policy, and pagination mechanics, all of which are present. The description is complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 43%, so the description must compensate, and it does for the undocumented parameters: cursor semantics (page continuation), sort_by semantics (latest vs popular), and region (always US). The remaining undocumented parameters, username, context, llm_model, and conversation_id, are either self-evident or fully specified inside the schema itself.

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?

States a specific verb and resource with scope: 'Fetch one cursor-paginated page of current TikTok videos for a username.' That distinguishes it from search_tiktok_videos and list_tiktok_hashtag_videos by implication (profile-scoped, paginated listing), but no sibling is actually named, so the differentiation is inferred rather than declared.

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 tells the agent how to continue ('use the returned cursor to paginate') and how to select ordering ('latest or popular'), which is real usage guidance. It gives no when-to-use-versus-alternative routing, e.g. nothing says to prefer get_tiktok_videos or list_tiktok_hashtag_videos under specific conditions, nor prerequisites for the account type beyond the private-account error case.

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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