frida-ios-mcp

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tiktok_reply

Sends a reply through TikTok's chat composer by typing the message and tapping send. Optionally use dry run to preview without sending.

Instructions

[advanced] Reply through TikTok's real chat composer. With dryRun:false, opens the chat, types into ChatInputTextView, taps 傳送, and succeeds only after the exact text is re-read from live message models.

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": {
      "text": "<text>"
    },
    "name": "tiktok_reply"
  }
}

Input Schema

Name	Required	Description
`text`	Yes	Reply text
`dryRun`	No	Default true — set false to send a real reply
`peerUid`	No	Fallback for Message Requests without a conversationId
`conversationId`	No	Conversation returned by tiktok_inbox
`confirmTimeoutMs`	No	Exact-text re-read timeout; default 15000, max 30000

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 27, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Without annotations, the description discloses the tool's operational flow: opens chat, types, taps send, and verifies by re-reading text. It mentions the dryRun flag to control destructive behavior. However, it does not explicitly state that sending a real reply is irreversible, nor does it cover potential side effects or failure modes (e.g., what happens if the chat is not open).

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?

The description is two sentences: the first states the tool's purpose, the second details its behavior. It is compact, front-loaded, and every word adds value without redundancy.

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

Completeness2/5

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

Given the absence of annotations and an output schema, the description should cover return values, error handling, and prerequisites. It mentions success criteria (re-reading exact text) but omits what the tool returns on success or failure, required preconditions (e.g., being in conversation), and potential errors.

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 individually documented. The description adds contextual value for the dryRun parameter and explains the overall process, but it does not deepen understanding of other parameters like peerUid or confirmTimeoutMs beyond their schema descriptions.

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 the tool is for replying through TikTok's real chat composer, specifying a verb ('Reply') and a resource. It implies uniqueness by mentioning 'real chat composer' but does not explicitly distinguish it from sibling tools like tiktok_im or tiktok_inbox.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives (e.g., tiktok_im, tiktok_inbox). It focuses on the internal mechanism (dryRun, chat composer) but lacks context about prerequisites or tool selection criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.