What data looks like for this category
Daily data for level content creation comes primarily from level editors, art resource libraries, player test data, and defect tracking systems. Daily documentation includes level design drafts, resource requirement sheets, test feedback forms, and version submission records. Fields are organized by level number and version number, including level ID, resource mounting path, feedback type, submission version, and other information. Data update frequency is typically weekly or when a version is submitted, and is dynamically adjusted alongside version iterations.
What constraints these characteristics impose on the "context and token" workflow
Data for this category is split by version and level dimension. Documents from different versions are highly correlated but cannot be used across versions. Therefore, context recall must be limited to the current version range to avoid invalid old data occupying token space. Document types vary widely, with single-document lengths ranging from short test feedback forms to detailed level path design drafts. Redundant information from irrelevant fields must be filtered to prevent total token usage from exceeding model limits. Data updates do not follow a fixed schedule, so context must be dynamically refreshed alongside version submissions. Otherwise, expired old version content may be recalled, harming the accuracy of content creation.
How to set configurations
The following configurations are based on the feature logic of FastGPT v4.17.0:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the total token volume of level-related documents for a single version, avoiding exceeding the context limit supported by the model |
quoteMaxToken | 800–1200 characters | Adapts to the average length of single level design drafts or test feedback forms, preventing a single reference from occupying excessive context space |
| Maximum single-round recall count | Top 6 entries | Limits total token consumption per recall round, avoiding exceeding the total length set by the context configuration |
| Context refresh trigger rule | Triggered by version submission | Aligns with data update frequency, ensuring recalled documents are valid for the current version |
| Context refresh interval | 7 days | Adapts to weekly update rhythms, preventing recall of expired old version data |
Three common configuration mistakes
- Setting
maxContextto a value exceeding the model's supported limit, resulting in a token limit exceeded error when calling the model, or forced truncation of output content. This occurs because the parameter is not adjusted based on the actual total token volume of single-version level documents, and an overly large value is used blindly. - Failing to configure
quoteMaxTokento limit single-reference length, causing a lengthy resource requirement sheet to occupy more than half of the context space, reducing the recall ratio of other valid documents. This happens because the wide variance in document lengths for this category is overlooked. - Failing to configure context persistence rules, resulting in context being reset after each MCP tool call, causing loss of level path configuration data across tool calls. This occurs because the business scenario of this category, which requires cross-tool linkage for level debugging, is not accounted for.
How to confirm proper configuration
- Upload a single typical level design draft and test feedback form, check the total token volume of recalled context documents, and confirm it does not exceed the
maxContextconfiguration range. - Trigger a context refresh after a version submission, and verify that all recalled documents are valid data for the current version, with no expired old version content.
- Call the MCP tool twice consecutively, check whether the context retains the level configuration data from the previous call, and has not been accidentally reset.
- Adjust the
quoteMaxTokenvalue, verify that the reference length of single documents complies with the configuration requirements, with no instances exceeding the limit.
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.