What Data for This Category Looks Like
Data for this category primarily comes from cloud database consoles, slow logs, monitoring metrics, and ticketing systems. Daily associated documents include instance change orders, backup and recovery records, performance diagnostic reports, and parameter adjustment records. Data updates trigger with changes or occur daily. Fields include instance ID, change content, performance metric values, ticket numbers, and more. Units include milliseconds, GB, count, and more. Most data is tied to the multi-service boundaries of cloud tenants, and data from different tenants must be strictly isolated.
Constraints Imposed on Context and Token Handling
Decentralized data sources require cross-system pulling during context aggregation, which increases token consumption. Diverse document structures (single change orders, bulk performance reports) require adaptation to context fragments of varying lengths. Multi-tenant boundaries require context isolation to prevent token contamination. High-frequency change data must be promptly included in the context, otherwise review accuracy will be impacted. High-frequency entries in slow logs may cause token overflow, exceeding the preset maxContext limit.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average total length of daily cloud database operation documents, prevents token overflow |
quoteMaxToken | 1500–2000 token | Limits token usage of a single associated document, prevents context overload |
| Context refresh timing | When an instance change is submitted | Matches data update frequency, ensures review data matches the current state |
| Multi-tenant context isolation | Grouped by tenant ID | Complies with cloud service multi-tenant boundary requirements, prevents cross-tenant context contamination |
| Single recall document limit | Top 3–5 entries | Balances context information volume and token consumption, adapts to bulk data from slow logs and change orders |
This document is compatible with FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: Model context is interrupted unexpectedly when calling MCP tools. Cause: No context boundary limit configured for
maxContext, causing excessive token usage during tool calls to trigger context reset. - Phenomenon: Uploaded operation document paths include extra tokens, preventing Flask from processing them. Cause: Temporary token parameters returned by the file upload interface were not cleaned, and the full path with the token parameter was passed directly to the business processing logic.
- Phenomenon: AI-generated review results contain non-standard JSON content. Cause: Non-standard formatted ticket or log data was mixed into the context, contaminating the model's output format.
How to Verify Proper Configuration
- View context aggregation logs, confirm only current tenant-related cloud database documents are pulled, with no cross-tenant data mixed in.
- Trigger an instance change review, check that no context interruption error occurs when calling MCP tools.
- Upload a performance diagnostic report, confirm the business system can obtain the file path without extra token parameters.
- Generate a review result, verify the output format matches the preset review report specification, with no abnormal JSON fragments.
The values provided are common starting points, and should be evaluated against your 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.