What the data for this use case looks like
Engineering consulting supervision ledger data primarily comes from on-site supervision logs, sub-project acceptance documents, material entry inspection forms, progress payment review files, and similar sources. Data update rhythm follows on-site construction milestones, with immediate updates after sub-project acceptance is completed. A single ledger document includes structured fields such as supervision unit, project location, acceptance time, material batch, qualification status, rectification and closure status, with attached on-site image attachments. Field units include standard engineering domain units such as square meters, ten thousand yuan, and calendar days.
Constraints on tool calling and plugins
Reference only; verify against the target FastGPT release and deployment documentation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapt to multi-tool calling requirements for supervision ledgers, automatically match tools for data pulling, format conversion, and other tasks |
| Single tool call timeout | 600 seconds | Supervision ledgers often include multiple attachments and complex fields, requiring sufficient time for data pulling and organization |
| Tool call retry count | 2 times | Avoid ledger pulling failures caused by on-site network fluctuations, while reducing unnecessary waiting |
| Markdown to file plugin | Set based on actual testing | Adapt to format requirements for supervision report generation, must match engineering document typesetting specifications |
functionCall | Enabled | Support structured parameter transfer, accurately match professional field verification for supervision ledgers |
| MCP tool call limit | First 3 entries | Control the number of patent or supplier information returned per call, to avoid result overload |
Three common configuration mistakes
- When calling a plugin that stores supervision ledgers in MongoDB, the
MongoServerError: The dollar ($) perror appears, and plugin confirmation fails. The cause is that the MongoDB version relied on by the plugin is incompatible with the FastGPT environment. The structured fields of supervision ledgers include special symbols, which trigger MongoDB syntax restrictions. - When calling an enterprise patent query tool via MCP, the tool set test returns normal results, but the component call returns that the enterprise has no patent information records. The cause is that the enterprise unified social credit code parameter was not passed correctly during component call, resulting in missing query conditions.
- When using the official Markdown to file plugin, "File upload failed" is displayed. The cause is that the file storage path permission required by the plugin was not configured, or the uploaded file size exceeds the maximum capacity allowed for supervision reports.
How to verify correct configuration
- Trigger a tool call, check if the returned ledger data fields include core professional fields such as supervision unit and project location, and verify that field units comply with engineering domain standards.
- Check plugin logs to confirm that the tool call timeout and retry count configurations have taken effect, with no frequent timeout or retry failure records.
- Run the Markdown to file plugin to generate a test report, verify that the typesetting format matches the general specifications for supervision reports, with no format disorder issues.
- Verify the MongoDB plugin connection, attempt to pull a single supervision ledger data, confirm that there are no permission or version errors.
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 2026-09-28.