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rpint

nfyy-ckd-risk-warning-mcp

by rpint

evaluate_risk_rules

Evaluates pediatric CKD risk using hard-coded SOP thresholds, returning warning level and triggered rules. Accepts patient ID for auto data retrieval or manual lab values.

Instructions

【硬规则引擎】按预警 SOP 硬阈值做确定性研判,返回预警等级与命中规则。

这是防止小模型算术幻觉的关键工具:涨幅计算、阈值比较、等级归并全部由代码完成。 Agent 必须以本工具返回的 level 与 triggered_rule_ids 为唯一判定依据。

两种用法:

  1. 传 patient_id:自动从模拟 LIS 拉取全部数据后研判(推荐)。

  2. 直接传指标数值:用于医生口述/临时录入的场景,未传的项按缺失处理。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
albuminNo本次血清白蛋白 g/L。
potassiumNo本次血钾 mmol/L。
age_monthsNo月龄,手工模式下用于血磷年龄分层,缺省按学龄期处理。
creatinineNo本次血肌酐 μmol/L。
patient_idNo患儿编号。传入时其余参数作为覆盖值,留空则完全使用手工输入。
phosphorusNo本次血磷 mmol/L。
specimen_noteNo标本备注,含「溶血」时会触发假性高钾复核规则。
height_z_seriesNo身高 Z 评分序列,格式同上。
weight_z_seriesNo体重 Z 评分序列,逗号分隔按时间升序,如 "-1.02,-1.28,-1.61"。
baseline_creatinineNo约 3 个月前血肌酐 μmol/L。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that arithmetic calculations are code-computed to prevent hallucination, that patient_id mode auto-fetches data, and that missing values are treated as missing. Also notes special handling for hemolyzed specimens. This offers valuable behavioral context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear intro, rationale, and bulleted usage modes. It is slightly longer than necessary but every sentence contributes context. The front-loaded purpose statement helps the agent quickly identify the tool's role.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 10-parameter complexity and the presence of an output schema, the description sufficiently covers when and how to use the tool, return types (level and triggered rules), missing value behavior, and special cases. Additional details on exact thresholds are appropriately left to the SOP.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already has 100% coverage with parameter descriptions. The description adds meaningful usage semantics by explaining the override behavior when patient_id is passed, the default handling for age/phosphorus in manual mode, and the special specimen_note trigger. This enhances understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a deterministic rule engine that evaluates against warning SOP hard thresholds, returning warning level and triggered rules. This specific verb-resource combination distinguishes it from sibling tools like fetch_patient_biochemical_trends or trigger_warning_event.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states two usage modes: passing patient_id for automatic LIS data pull (recommended) or direct metric values for manual entry. It also instructs the agent to rely solely on this tool's output for risk judgment. Does not explicitly contrast with alternatives, but context makes it clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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