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AdsAgent — TikTok Ads MCP

Create an insight CSV export

insights_export_csv

Export insights as CSV. export_mode='grouped' emits per-entity totals over the window; export_mode='daily' emits one row per (date, entity). Requires date_from, date_to, one scope filter (ad_account_id or product_id), and confirm_export=true. Returns an artifact_ref and authenticated download_endpoint valid for one hour (maximum 5 MiB). GET the endpoint on this server with the same tenant Bearer credential; never append credentials to URLs. Attribution: omit or value (TikTok configured window).

Persists a private, expiring CSV artifact; this is not a read-only call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNo
date_toNo
group_byYes
date_fromNo
product_idNo
attributionNo
export_modeNogrouped
ad_account_idNo
confirm_exportNo
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

A4.2/5.0
Behavior5/5

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

Goes well beyond the annotations: discloses the artifact_ref + one-hour download_endpoint contract, the 5 MiB cap, same-tenant credential requirement, the rule never to embed credentials in URLs, and the attribution options. The closing line 'this is not a read-only call' correctly reinforces readOnlyHint=false without contradicting it.

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?

Purpose is front-loaded and the two paragraphs are dense with actionable constraints. There is mild redundancy between the return-value sentence and the closing 'private, expiring CSV artifact' line, but each statement still adds distinct operational guidance.

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?

For a 10-param export tool with no output schema, the description covers the return shape, auth model, size/time limits, and required inputs. The main gap is the required group_by parameter and search filter, which an agent must infer from the schema alone.

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?

With 0% schema coverage the description carries the load and explains date_from, date_to, the ad_account_id/product_id scope choice, confirm_export, export_mode, and attribution semantics. However, group_by (a required enum of account/campaign/ad_group/adset/ad) and search are left unexplained.

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+resource ('Export insights as CSV') and immediately clarifies the two emission granularities (grouped per-entity totals vs. daily date/entity rows). The CSV-artifact framing makes it distinguishable from the many insights_* query siblings, though none are named explicitly.

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?

Gives concrete prerequisites (date_from, date_to, exactly one scope filter, confirm_export=true) and explains which export_mode to pick for which output shape. It stops short of explicitly routing the agent away from siblings like insights_query_rows or insights_pull_insights.

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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