Tool Calling and Plugins for Rehabilitation Device

Data Characteristics in this Category

Rehabilitation device pharmacovigilance data primarily originates from post-market surveillance reports, clinical trial data, patient feedback, and healthcare institution reports. This data typically exists as a mix of structured and unstructured formats. Structured data includes event dates, device models, serial numbers, event types (e.g., adverse events, product defects), patient demographic information, and event description codes (e.g., MedDRA codes). Unstructured data comprises detailed event descriptions, healthcare professional notes, patient self-reports, and imaging data. Data update frequency is high, especially during initial product launches or when major safety alerts are issued. Document structures vary, including standardized report forms, free-text medical records, PDF investigation reports, and image or video evidence. Regarding fields and units, event descriptions may involve device operating parameters (e.g., pressure in kPa, angle in °), patient physiological indicators (e.g., heart rate in bpm, blood pressure in mmHg), and event timestamps.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The diversity and update frequency of rehabilitation device data impose specific requirements on tool calling and plugins. Structured data is easily queried and filtered directly via APIs. However, unstructured data requires more complex text parsing and semantic understanding capabilities. For example, processing vague descriptions in patient self-reports necessitates plugins with natural language processing capabilities to extract key information. High update frequency means tool calling must support real-time or near real-time incremental data synchronization to avoid information lag. Diverse document structures require plugins to handle various input formats, such as extracting specific fields from PDF reports or identifying device label information from images. Furthermore, the specialized units and numerical ranges of device operating parameters and physiological indicators demand that tool calls accurately identify and process these specific dimensions during data validation and anomaly detection, preventing misjudgments due to unit confusion. The global variable passing mechanism must ensure consistency across different data sources and processing steps, particularly in complex scenarios involving multi-device linkage or multi-report correlation.

Configuration Strategy

Configuration Item	Recommended Value	Rationale
`maxContext`	`2000 characters`	Captures core information of typical adverse event descriptions completely, balancing processing efficiency.
`PARSE_FILE_TIMEOUT_SECONDS`	`180 seconds`	Accommodates the time required for large PDF reports or multi-page image processing, preventing parsing failures due to timeouts.
`Similarity threshold`	`0.85`	Ensures highly relevant text is effectively identified during adverse event description matching, reducing false positives.
`Chunk size`	`500 characters`	Optimizes vectorization effects for unstructured text, balancing context completeness and retrieval granularity.
`Rerank result count`	`Top 5 entries`	Focuses on the most relevant adverse event records, assisting engineers in quickly identifying issues.
`mcpserverproxyendpoint`	`http://localhost:8080` or `http://[IntranetIP]:[Port]`	Ensures the MCP toolset can correctly connect to a locally deployed MCP Server instance, providing localized data processing capabilities.

Three Common Mistakes

Global variable passing fails or is empty when calling external platform interfaces, leading to interruption of subsequent processing logic or inaccurate results. This occurs because the external platform's interface call does not correctly encapsulate or map global variables defined in the FastGPT workflow.
The text content extraction tool consistently reports parsing errors, failing to obtain key fields from rehabilitation device reports. This happens due to diverse report formats, where the tool's configured parsing rules do not match the actual document structure, or issues with OCR for scanned images within PDF files are not handled.
Connection timeouts or refused connections occur when calling the local MCP Server, preventing local tools from processing data. This is caused by an incorrect mcpserverproxyendpoint configuration, not pointing to the correct IP address and port, or a local firewall blocking the connection.

Confirmation of Correct Configuration

Observe tool call logs in FastGPT's debugging interface. Confirm all external API calls return a 200 status code, and request and response bodies contain expected data.
Upload a typical rehabilitation device adverse event report (containing both structured and unstructured content). Verify that the text content extraction tool accurately identifies and extracts all predefined key fields, with no empty values or garbled characters.
Simulate an external interface call involving global variables. Check that the values of global variables in the FastGPT workflow exactly match the values passed externally.
Execute a function involving the MCP toolset. Confirm the local MCP Server responds normally and returns processing results in the expected format.

The values provided are common starting points. Measure against specific samples to refine them.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.