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AdsAgent — TikTok Ads MCP

Prepare an optimization decision

optimization_prepare_action
Destructive

Prepare the exact management action for one open owned decision. The server performs a fresh native TikTok read on the original advertiser route. Show the sanitized approval and obtain explicit confirmation with the returned delivery_status_confirm or budget_confirm tool. Never auto-confirm or replay uncertainty.

Persists an approval draft and replaces the decision's preparation state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
decision_refYes
idempotency_keyYes
connection_contextNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / connection_context
      Added value: +{
      +  "pattern": "^mctx_v1_[0-9a-f]{64}$",
      +  "type": "string"
      +}
  2. First observed

TDQS

B3.4/5.0
Behavior4/5

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

Beyond the destructiveHint/openWorldHint annotations, the description discloses the server-side behavior: a fresh native TikTok read on the original advertiser route, persistence of an approval draft, and replacement of the decision's preparation state. This is consistent with destructiveHint=true and adds genuine behavioral context the annotations do not carry.

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?

Two short paragraphs, front-loaded with the core action and followed by the safety directive and the state-change note. Nearly every sentence carries weight, with only mild redundancy between the approval/confirmation instructions.

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

Completeness3/5

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

For a destructive mutation the description does well on workflow and state change, and annotations cover the safety profile. But with no output schema and 0% parameter coverage, the tool's inputs remain unexplained, leaving a real gap an agent must fill from the raw schema.

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

Parameters2/5

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

Schema description coverage is 0% across three parameters, yet the description does not explain decision_ref, idempotency_key, or connection_context. It only alludes to 'one open owned decision,' leaving the idempotency and connection-context semantics entirely undocumented.

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 states a specific verb and resource: 'Prepare the exact management action for one open owned decision,' which is distinct from optimization_list_decisions or optimization_evaluate. It is clear what the tool does, though it never explicitly contrasts itself with the sibling optimization tools that also prepare/confirm actions.

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 routes the agent forward, instructing it to obtain confirmation via delivery_status_confirm or budget_confirm and never auto-confirm, which is useful workflow context. However, it gives no criteria for choosing this tool over optimization_evaluate or the other *_prepare tools, so the when-to-use guidance is only implied.

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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