What the data for this use case looks like
The data for urban commercial bank research reports comes from three main sources: the regional economic research center, the risk management department’s in-region business ledgers, and public industry data from licensed regional financial information service providers. Data update cadence falls into three categories: internal in-region business data is updated monthly, regional macro thematic research reports are released quarterly, and policy interpretation content is updated weekly. A single research report’s document structure includes three core modules: regional economic indicators, in-region enterprise credit summary, and interbank liability structure analysis. Fields include in-region small and micro enterprise loan balance (unit: 100 million yuan), non-performing loan level (unit: percentage points), regional economic growth rate (unit: percentage points), among others. Some internal ledger data is embedded in documents as structured tables.
What constraints do these data characteristics impose on tool calling and plugins
These data characteristics impose specific constraints on tool calling and plugins. Multi-source data sources require tool calling plugins to support mixed calls to internal APIs and third-party REST interfaces, avoiding limitations of single data sources. Field-specific unit requirements mandate unit verification for returned data during tool calls, preventing analysis deviations caused by incorrect parameter transmission. Differentiated update cadences require plugins to support incremental synchronization mechanisms, eliminating full data pulls each time and reducing call latency. The long-text nature of single research reports requires tool calling context lengths to adapt to large-scale recall and processing. Plugins must also support custom filtering by region and industry dimensions, accurately matching the business analysis needs of urban commercial banks.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MCP_SERVER_URL | http://localhost:8080/mcp | Adapts to the official default MCP service startup port, supports local debugging and online deployment scenarios |
TOOL_CALL_MAX_RETRIES | 3 retries | Balances single-call success rate and overall process time, prevents task interruptions caused by single failures |
MAX_CONTEXT_LENGTH | 8000-12000 characters | Adapts to the average length of a single urban commercial bank research report, meets context requirements for long-text recall and tool calling |
FILTER_FIELD_LIST | ["in-region loan balance", "non-performing loan level", "regional economic growth rate"] | Matches core business fields of urban commercial bank research reports, filters irrelevant content to improve analysis accuracy |
PLUGIN_UPDATE_INTERVAL | 86400 seconds | Adapts to the monthly update cadence of urban commercial bank research report data, daily synchronization ensures data timeliness |
TOOL_TIMEOUT_SECONDS | 600 seconds | Covers reasonable time for research report data pulling and structured processing, prevents tool calling processes from timing out and interrupting |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on reader's own samples before finalizing.
Three Common Mistakes
- Symptom: After startup, the MCP tool cannot be recognized by the platform, and the interface returns a 404 status code. Cause: After upgrading
fastgpt-mcp-server, theMCP_SERVER_URLconfiguration item was not updated, causing the platform to fail to connect to the new service port. - Symptom: The LLM only calls the last configured tool in a single query, and does not execute in the expected order. Cause: The
SEQUENTIAL_TOOL_CALLconfiguration item was not enabled, and the sequential triggering logic for tool calls was not specified. - Symptom: Non-target fields are mixed into the research report data returned by the tool, causing the LLM to generate conclusions that deviate from business requirements. Cause: The
FILTER_FIELD_LISTparameter was not configured, and precise filtering of recalled fields was not performed.
How to Confirm Configuration Is Correct
- Visit the address configured in
MCP_SERVER_URL, check whether a valid tool metadata interface response is returned. - In the platform's tool debugging interface, enter query terms related to urban commercial bank research reports, trigger tool calls, and check whether the returned fields match the
FILTER_FIELD_LISTconfiguration. - Simulate a multi-tool call process, confirm that the LLM will call tools sequentially according to the configured logic and integrate results.
- Restart the platform service, check whether the run logs include signs of successful MCP service connection.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.