What the data for this category looks like
The core data for this category comes from search ad backends, search term reports, bidding ranking data, and landing page data. Most updates occur on a daily or weekly basis. Standardized documents produced regularly include keyword lists, negative keyword tables, bid records, and search term reports. Each document contains fixed fields such as keyword text, matching mode, single bid amount, click count, and conversion cost. Units are uniformly basic units such as CNY, times, and characters. No additional non-preset custom fields are included.
What constraints these characteristics impose on the tool calling and plugins workflow
The data for this category comes from multiple dispersed sources, requiring integration with various ad backend APIs. The tool calling workflow must support configuration for multiple independent endpoints, to prevent a single API failure from disrupting the overall keyword management process. Standardized fields in regularly produced documents have high consistency but cover many dimensions. The tool calling workflow must explicitly specify the range of fields to extract, to avoid returning redundant data that consumes context. Data updates occur at relatively high frequencies. The tool calling workflow must adapt to daily or weekly batch processing rhythms, and also configure real-time monitoring mechanisms to promptly detect issues such as invalid clicks and abnormal conversion costs.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Tool calling mode | Configured as functionCall | Meets structured extraction requirements for search ad data, avoiding field confusion caused by free-form output |
| Tool calling trigger timing | Daily scheduled trigger | Matches the daily update rhythm of search ad backend data, ensuring processing of the latest keyword and bid data |
| Single tool call timeout | 600 seconds | Accommodates parallel request latency across multiple ad APIs, preventing task failure due to API delays |
| Maximum number of tool return results | Top 20 entries | Matches the reasonable processing scale for single-batch keyword management, preventing excessive returned data from consuming context |
| Error retry count | 3 times | Addresses temporary fluctuations in ad APIs, reducing task interruptions caused by short-lived network issues |
| MCP service proxy endpoint | Configured using the full ad backend API path and authentication parameters | Ensures tool calling can correctly connect to data sources from search ad backends |
Three common mistakes
- Google Search plugin returns no results. The cause is that context parameters for search ad keywords are not correctly bound, leading to the correct search term range not being passed during tool calling.
- MCP service proxy endpoint configuration does not take effect. The cause is that the full API path and authentication parameters are not filled in, preventing normal connection to the search ad backend API.
- No response after tool calling is triggered. The cause is that the forced calling mode for the
toolChoiceparameter is not enabled, leading the large language model to not trigger the tool calling workflow.
How to confirm the configuration is complete
- Manually trigger a tool call, and check if the returned results include preset fields such as the correct keyword list and bid records.
- View the tool calling logs in FastGPT v4.17.0, confirm that the
functionCallparameter has been correctly enabled with no format errors. - Configure a scheduled trigger task, wait for one update cycle, and check if the latest search term report data is automatically pulled.
- Use the Qwen2.5-14B model to test tool calling, confirm that the model can correctly trigger the structured tool calling workflow.
- Simulate an API error scenario, confirm that the error retry mechanism triggers as configured, and the task does not terminate directly.
The values provided are common starting points and should be measured against the reader's own 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.