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AdsAgent — TikTok Ads MCP

Read cached insight rows

insights_query_rows
Read-only

Raw insight rows (capped at 5000) for the authenticated tenant. Use for debugging or detail drilldown; query_overview is the main read path.

REQUIRED: date_from, date_to, and one scope filter (ad_account_id or product_id). DATE PARAMS: use date_from / date_to (NOT start_date / end_date). EXAMPLE: insights_query_rows({"ad_account_id": "<advertiser_id>", "date_from": "2026-04-23", "date_to": "2026-04-29"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_toNo
date_fromNo
product_idNo
ad_account_idNo
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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so safety is covered. The description adds genuine behavioral context beyond that: the 5000-row cap and the tenant-scoping constraint. It stops short of describing pagination or return shape, but the cap is a meaningful trait.

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?

Front-loaded purpose sentence, then labeled sections (REQUIRED, DATE PARAMS, EXAMPLE) that make the critical constraints scannable. The example earns its place by demonstrating the required-parameter combination and date format.

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 read-only, annotation-covered tool with no output schema, the description supplies what the schema omits: required params, naming caveat, row cap, and sibling routing. Only the connection_context parameter and any row-shape expectations are unaddressed.

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 0% and all five params are nullable with defaults, so the schema implies nothing is required. The description compensates strongly: it declares the actual required set (date_from, date_to, and one of ad_account_id/product_id), warns against the start_date/end_date naming trap, and supplies a concrete call example. connection_context is left unexplained, keeping this from a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (raw insight rows) with its scope (authenticated tenant) and an explicit cap (5000), then contrasts directly with the sibling query_overview as 'the main read path.' An agent can tell this apart from the other insights_* tools without opening the schema.

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

Usage Guidelines5/5

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

Explicitly routes usage: 'Use for debugging or detail drilldown; query_overview is the main read path.' This names both the when-to-use and the preferred alternative for the common case, leaving nothing to inference.

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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