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TikTok Ads MCP by InsightfulPipe

by com.insightfulpipe
OverviewDiscussionsAdmin

TikTok Ads Prompts

prompts_menu
Read-only

Access TikTok Ads prompt catalog with 4 operations:

  1. List Public Prompts: • request='menu' - No additional params needed • Returns: All public/system prompts available for TikTok Ads

  2. List Custom Prompts: • request='menu', prompt_type='custom', workspace_id= • Returns: All custom prompts for the specified workspace

  3. Get Public Prompt Details: • request='prompt_details', prompt_id= • Returns: Full text/details of a public prompt

  4. Get Custom Prompt Details: • request='prompt_details', prompt_id=, is_custom=True, workspace_id= • Returns: Full text/details of a custom prompt

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": {
      "request": "<request>"
    },
    "name": "prompts_menu"
  }
}

Input Schema

NameRequiredDescriptionDefault
requestYes
is_customNo
prompt_idNo
prompt_typeNo
workspace_idNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

readOnlyHint=true is consistent with a catalog-reading tool, and the description adds behavioral detail the annotation does not: the public/custom split, the workspace scoping requirement for custom prompts, and that detail modes return full prompt text. It does not discuss pagination or limits on list results.

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

Conciseness4/5

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

A front-loaded operation count followed by four parallel numbered blocks with consistent 'request/params/returns' structure makes it skimmable. Slightly verbose with repeated 'Returns:' and bullet-separated params, but nothing is wasted.

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

Completeness4/5

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

With an output schema present, the description needn't detail return values, and it covers all four call modes plus their required parameters. The main residual gap is list behavior (pagination/ordering) for the menu modes.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must carry the parameter burden, and it does: all five parameters (request, prompt_type, is_custom, prompt_id, workspace_id) are mapped to the specific operation that uses them. This is exactly the mode-to-parameter mapping an agent needs to avoid mis-invocation.

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 enumerates four concrete operations (list public, list custom, get public details, get custom details) on a TikTok Ads prompt catalog, so the resource and the actions are unambiguous. It stops short of naming the siblings (query_data, execute_action) or stating how this differs from them.

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

Usage Guidelines4/5

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

Each mode comes with the exact parameter combination that selects it (e.g. request='menu' plus prompt_type='custom' plus workspace_id for custom lists), which is real invocation guidance rather than inference. There are no when-not-to-use conditions or explicit alternatives to the sibling tools.

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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