fetch_patient_biochemical_trends

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

No annotations are provided, so the description carries the burden. It discloses the output contents (current results, baseline, change percentages, Z-scores) and reveals the mock nature and future data-source replacement, indicating it is a safe, read-only simulator. It does not explicitly state 'no side effects', but the fetch/simulation context makes this transparent.

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

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

Two sentences, front-loaded with a label and immediate description of the resource. No filler words. The mock prefix and the data structure explanation are both purposeful.

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

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

Given an output schema exists, the description needn't detail return values, but it does anyway, enhancing clarity. It addresses the tool's purpose, mock nature, and the fields included. The tool is simple (2 params) and this description is sufficient.

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

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

The schema describes both parameters (patient_id, months) with 100% coverage. The description does not add significant meaning beyond what the schema already states, though it connects 'months' to the historical baseline in the output. Baseline is 3 due to high schema coverage.

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

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

The description clearly states the verb '获取' (fetch) and the resource '患儿生化指标趋势数据包' (pediatric biochemical trend data package). The '南向 Mock 工具' prefix distinguishes it as a mock LIS tool, differentiating it from sibling tools like list_mock_patients or evaluate_risk_rules.

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

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

The description provides clear context by stating it simulates the hospital LIS system and returns trend data, implying when to use it (when LIS trend data is needed). It does not explicitly name alternatives or exclusions, but sibling tools are clearly for different purposes. The schema references list_mock_patients as a prerequisite, hinting at a workflow.

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