Tool Calling and Plugins for Film Theater Intelligent Due

What the data for this category looks like

Data for film theater intelligent due diligence reports comes from three main sources: the National Film Ticketing Comprehensive Information Management System, theater-owned operation backends, and third-party film and television data platforms. Scheduling and operational data is updated daily. Box office data is synchronized after the day’s business closes. Documents primarily use structured JSON and CSV formats. Fields include unique film identifier, cinema name, scheduled screenings, average per-screening attendees, per-screen box office, and release cycle. Units are screenings, attendees, yuan, and calendar days respectively. Some data has cross-platform synchronization delays. Calling timing must be adjusted based on individual data source characteristics.

Constraints imposed on tool calling and plugins

The multi-source nature of film theater due diligence data requires tool calling to connect to multiple MCP services at once. Each service has unique input parameter validation rules. Request headers and parameter formats must be configured separately for each. The daily update cadence requires tool calling’s scheduled trigger period to match the data synchronization window. This avoids sending invalid requests. Clear field and unit requirements mean plugin output parsing logic must bind fixed field names and unit mappings. This prevents data parsing errors. Some data has synchronization delays. Timeout wait parameters must be added to tool calling configurations to ensure complete daily data is retrieved.

How to set configurations

Configuration Item	Recommended Setting	Rationale
`MCP_ENDPOINT`	Fill in the official API addresses of the ticketing system and third-party film and television data platforms separately	Match the docking requirements of multi-source data
`MCP_API_KEY`	Exclusive keys applied for from each data source	Meet permission verification requirements of different platforms
`tool_call_cron`	`0 2 * * *`	Match the window when that day's box office data synchronization completes
`parse_field_mapping`	Bind fields and units such as `影片ID`→`film_id`, `单银幕票房`→`screen_box_office`	Ensure accurate structured data parsing
`request_timeout_seconds`	`600 seconds`	Adapt to synchronization delay windows of some data sources
`rate_limit_per_minute`	`10 requests`	Comply with call frequency limits of most film and television data platforms

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to perform testing on available samples before finalizing settings.

Three common mistakes

Phenomenon: The MCP service configuration page cannot add HTTP response input parameter variables; fields are empty after saving. Cause: The "Enable dynamic parameters" switch is not checked, so custom input parameters cannot be recognized by the system.
Phenomenon: The large language model does not trigger tool calling, and returns empty tool calling results. Cause: The tool_call_cron trigger condition is not configured, or the trigger period does not match the data synchronization window, so no valid data is available for calling.
Phenomenon: Calling the FastGPT chat interface in Python returns a 401 Unauthorized error. Cause: The Bearer token for the Authorization field is not correctly carried in the request header, or the token has expired and is invalid.

How to confirm configurations are correct

Access the MCP service management page, verify that MCP_ENDPOINT and MCP_API_KEY for each data source are filled correctly. Click the test connection button to confirm the return status code is 200 OK.
Run a test script that manually triggers tool calling, check that the returned structured data includes preset field names and units, with no missing or incorrect mappings.
View the workflow routing rule configuration page, confirm that trigger keywords match routing conditions. Test that after triggering, tool calling and knowledge base recall results are properly separated.
View system logs, confirm that interface call frequency does not exceed the set threshold of rate_limit_per_minute, with no 429 Too Many Requests errors.

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.