What this category of data looks like
Daily data sources for level content creation include level editors, art resource libraries, player test data, and defect systems. Update cadence is weekly or per-version iteration. Daily documents include level design drafts, resource requirement sheets, test feedback forms, and version submission records. Level design drafts contain structured fields such as path node coordinates, resource IDs, and trigger conditions. Test feedback forms contain unstructured fields such as stuck positions and player feedback content. Version submission records track the content of each change and the information of the submitter.
What constraints do these characteristics impose on the context and token workflow
The mixed structured and unstructured nature of level content data requires distinguishing between structured version submission records and unstructured test feedback in the context, to avoid irrelevant data consuming tokens. The weekly or per-version update cadence means the context does not need to retain more than 1-2 versions of historical data. Otherwise, token overflow will occur and invalid content will be introduced. A large amount of long text content such as coordinates and resource paths increases the token consumption of a single context. The total length of the context and the number of recalled entries must be strictly controlled to avoid exceeding the model's token limit. At the same time, changes to level content directly affect game experience. The context must accurately match valid data for the current version, otherwise change review results will deviate from actual requirements.
How to configure the settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Level content data includes long text such as coordinates and resource paths. A single context needs to cover 1-2 versions of submission records and test feedback, calibrated via actual testing |
quoteMaxToken | 2000–3000 token | Referenced level design draft fragments and test feedback form content are lengthy, to prevent a single reference from occupying too much context space |
| Context recall count | Top 6–10 entries | Match recent version submissions and test feedback, avoid excessive old data consuming tokens |
| Historical context retention period | Reset every 7 days | Level content is updated weekly or per version, old version data does not need to be retained in the current context |
| Content similarity threshold | 0.7–0.8 | Filter documents strongly related to the current level task, exclude irrelevant general content from the art resource library |
Three common mistakes
- Phenomenon: When calling the model to query level data, the token consumption displayed in the FastGPT interface does not match the token consumption of the actual API call. Cause: The token counted by the interface includes consumption from context embedding and preprocessing links, while the API only counts token consumption from model inference, and there is a difference in the scope of statistics between the two.
- Phenomenon: The context returned during change review contains a large amount of old version level data, causing the model output to deviate from the current version content. Cause: The historical context retention rule is not configured, so old version level design drafts and test feedback are continuously included in the context, occupying too many tokens and introducing invalid information.
- Phenomenon: When configuring an MCP tool in a workflow, the workflow global variable token cannot be passed. Cause: The global token variable is not bound to the input parameter configuration item of the MCP tool, and the context variable is not correctly mapped to the tool call parameters.
How to confirm the configuration is correct
- Open the context preview panel in FastGPT v4.17.0, confirm that all recalled documents are current version level design drafts and test feedback forms, with no old version irrelevant content.
- Run a workflow that includes an MCP tool, confirm that the workflow global variable token can be normally passed to the input parameter fields of the tool.
- Compare the token consumption displayed in the interface with the token consumption returned by the actual API, confirm that the statistical difference between the two conforms to the configuration rules.
- After adjusting context-related configurations, verify whether the output result of the change review focuses on the current level's change content, with no redundant information.
The values provided are common starting points. They should be validated against local sample data.
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.