Test Cases and Defect Analysis for Feed Ad Creative Testing: Tool Calling and Plugins

Core data for this category comes from feed ad platforms, asset libraries, user interaction data, and conversion data. Most updates run on a daily or

This document is written for FastGPT v4.17.0.

What the data for this category looks like

Core data for this category comes from feed ad platforms, asset libraries, user interaction data, and conversion data. Most updates run on a daily or weekly schedule. Common daily documents include creative scripts, asset test sheets, delivery data reports, and iteration checklists. The asset test sheet includes fields such as creative type, variable combinations, delivery time slots, interaction metrics, and violation flags. Delivery data reports track core business metrics. All data centers around the test performance of creative combinations.

What constraints these characteristics impose on tool calling and plugins

Daily work centers on structured test sheets and reports. Tool calling must accurately read target fields to avoid pulling invalid data. Data sources are scattered, and update schedules are fixed. Configure trigger-based or scheduled tool calling logic to align with data update cycles. Creative variable design, fatigue assessment, and conclusion summarization are the most time-consuming steps. Tool calling must automatically generate multiple test case sets, compare asset interaction data to judge fatigue levels, and integrate violation detection logic to mitigate business risks.

How to set configurations

Configuration ItemRecommended SettingRationale
Tool call trigger timingTriggered upon completion of asset test sheet updatesDaily test workflows use asset test sheets as the core carrier. Launch case generation and analysis immediately after updates are complete.
toolChoiceautoAdapts to tool calling requirements for different types of creative tests, and automatically selects compliant function call logic.
Maximum concurrent tool callsTop 10 entriesMatches standard call limits for feed ad platforms, and avoids triggering frequency restrictions.
Result output formatStructured JSON formatEnables easy passing to subsequent AI conversation modules, and direct extraction of business fields such as click-through rate and conversion cost.
Timeout duration600 secondsCovers standard time for asset pulling and data aggregation, and prevents call interruptions caused by fluctuations in data volume.
Violation detection thresholdCalibrated via actual testingAdapts to compliance rules for different feed ad platforms, and avoids misjudging or missing violations in assets.

Three common mistakes

  • Symptom: An "invalid request address" error is returned when calling the OpenRouter API. Cause: The officially recommended request endpoint is not used, and a generic API address is entered incorrectly.
  • Symptom: 429 status codes are triggered frequently during concurrent tool calls. Cause: Concurrency is not limited, exceeding call frequency limits for third-party interfaces or ad platforms.
  • Symptom: Association information for the chatId parameter is not recorded in conversation logs. Cause: The chatId field is not passed correctly in tool call requests, or log collection configuration for conversation parameters is not enabled.

How to confirm configurations are set correctly

  • Submit a fully edited asset test sheet to trigger tool calls. Check if the correct request address and parameters appear in tool call logs.
  • Simulate multiple concurrent tool calls. Check if frequency restriction errors are triggered, and confirm that concurrency settings meet the limit requirements of the corresponding interface.
  • Trigger tool calls, then check the context window of the subsequent AI conversation module. Confirm that structured business data returned by the tool is included.
  • Check conversation logs. Confirm that the chatId parameter is correctly recorded and associated with the current call task.

The configuration values listed here are common starting points. They should be evaluated against local test samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.