What this category of data looks like
Data sources include approved declaration documents, environmental impact assessment reports, safety supervision filing files, and internal approval circulation archives for coal-fired power integration projects. Updates occur via batch upload during the project declaration phase, with incremental updates along approval nodes during the review process. Most documents are in PDF or Word format, with single document lengths ranging from dozens to hundreds of pages. They contain fixed fields such as project overview, coal resource reserves, installed capacity, environmental governance plan, and approval flow table. Field units are mostly tons of standard coal, ten thousand kilowatts, and ten thousand yuan, with uniformly formatted approval document numbers and project codes attached.
What constraints these characteristics impose on tool calling and plugins
Multi-page long documents require tool calling to support long context parsing, to avoid truncating key approval fields. Fixed field structures require precise field extraction rules configured for tool calling, to ensure extraction results comply with state-owned enterprise and public sector approval specifications. Phased updated data requires plugin trigger timing to be bound to project approval nodes, only triggering tool calling at corresponding nodes. Confidential internal data requires plugins to connect to internal unit permission verification interfaces, to restrict unauthorized access. Large single document data volume requires configuring reasonable timeout and token thresholds for tool calling, to adapt to internal system response durations.
Configuration Settings
| Config Item | Suggested Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to different types of tool calling requirements in coal power declaration materials, automatically matching document parsing, field extraction, and system integration scenarios |
functionCall | Enabled | Forces the model to output results in preset function format, ensuring standardization of declaration material field extraction |
| Tool calling timeout duration | 120 seconds | Matches the average interface response duration of internal approval systems for coal-fired power integration projects |
| Maximum token count per single tool call | 8000–10000 token | Adapts to the average length of single coal power declaration materials |
| Field extraction template | Configured according to coal power project approval standard templates | Matches the fixed field structure of declaration materials, ensuring extraction accuracy |
Three Common Errors
- Phenomenon: Tool calling node output is interrupted, with logs showing token limit exceeded. Reason: Maximum token count per single tool call is configured lower than the actual length of a single coal power declaration material, causing truncation during model parsing.
- Phenomenon: Duplicate thinking process logs appear twice when debugging the workflow. Reason: When
toolChoiceis set to auto mode, the model automatically switches tool calling logic twice, resulting in duplicate output. - Phenomenon: Empty fields are returned when calling the API to obtain MCP tool execution results. Reason: The MCP tool and the corresponding project's permission interface are not bound in the workflow, causing permission verification failure and failed tool call execution.
How to Confirm Proper Configuration
- Upload a standard-format coal power declaration material, run the workflow, and check if the tool calling node successfully extracts specified fields such as approved reserves and installed capacity.
- View workflow run logs, confirm that the
toolChoiceandfunctionCallconfiguration parameters match the preset values. - Call the FastGPT v4.17.0 workflow API to trigger execution, check if the returned results include complete tool call output fields.
- Simulate a timeout scenario, confirm that the tool calling correctly triggers error reporting or retry logic after 120 seconds.
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.