What the data for this category looks like
Data for the model allocation category is sourced from public API documentation of connected large model service providers and real-time call verification results. There are two update modes: automatic pull and manual refresh. Automatic pull runs once per hour. Manual refresh triggers real-time synchronization immediately. Each data entry is a structured collection of configuration items, including fields such as model_id, provider, max_context_tokens, request_rate_limit, default_timeout. The unit for max_context_tokens is token. The unit for request_rate_limit is requests per minute. The unit for default_timeout is seconds. All fields use unique, non-repeating identifiers paired with their corresponding parameter values.
What constraints these characteristics impose on the tool calling and plugins workflow
The structured unique identifier fields for model allocation require that tool calling requests use a precisely matched model_id. Fuzzy model names cannot trigger the corresponding model. The automatic update cycle requires manual data synchronization when adding or adjusting connected models during plugin configuration, to ensure normal call links. Differences in request_rate_limit and default_timeout parameters across models require separate configuration of rate limiting thresholds and timeout values for each allocated model during tool calling. This prevents call failures caused by parameter mismatches. Differences in max_context_tokens require plugins to truncate input text based on the window limit of the allocated model when splicing content. This prevents exceeding the model's processing upper limit.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
assigned_model_id | Select the precisely matched unique identifier from the model list synchronized by the platform, for example gpt-4o, claude-3-sonnet, gemini-pro-1.5 | Matches the unique model identifier stored by the platform to avoid call link parsing failures |
tool_call_timeout | 30-60 seconds | Covers the conventional response duration for most large model tool calls, balances waiting efficiency and call success rate |
max_context_tokens_per_call | Set according to the official window value of the allocated model, for example 128000 (for gpt-4o), 200000 (for gemini-pro-1.5) | Matches the context processing upper limit of the allocated model, prevents input text overflow causing call failures |
request_rate_limit | Set according to the public quota of the model service provider, for example 60 requests per minute | Complies with the rate limiting rules of model calls, avoids triggering call restrictions from the service provider |
plugin_trigger_mode | Configure according to the business logic of tool calling, for example Trigger when user questions contain retrieval requirements | Accurately matches the trigger scenario of tool calling, reduces invalid call times |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Tool calling returns a
400 Bad Requesterror, and theassigned_model_idfield is empty in the logs. Cause: No precise model allocation ID is specified in the configuration, fuzzy names are used, and the unique identifier synchronized by the platform is not used. - Symptom: Some requests trigger a
429 Too Many Requestsstatus code during multi-model parallel calls. Cause: Therequest_rate_limitparameter is not configured separately for each allocated model, and a unified rate limiting threshold is used instead. - Symptom: Bing Search plugin returns no results after being called, and retrieval cannot be triggered in domestic environments. Cause: No domestic environment-adapted search plugin is used, and the default plugin configuration dependent on overseas services is used directly.
How to confirm the configuration is complete
- Enter the model allocation management page of the platform, check that
assigned_model_idmatches the unique identifier in the platform-synchronized model list. - Initiate a test tool call, check if the returned logs include the allocated model's
model_idfield and the calling parameters of the corresponding plugin. - Adjust the length of the test input text, check if the context truncation logic is triggered, and it complies with the window limit of the allocated model.
- Simulate a high-frequency call scenario, observe if rate limiting-related logs are triggered, and confirm that the
request_rate_limitconfiguration takes effect.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.