get_lab_trend

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 core behavioral details: returns a time series with computed metrics (环比变化率 and 30-day slope). However, it does not mention whether the operation is read-only, if any side effects occur, or how window_days affects the output. This is moderate transparency but leaves gaps.

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?

The description is a single, information-dense sentence, front-loading the core purpose and immediately adding value with analyte examples. There is no fluff or redundancy; every clause contributes to understanding the tool's output and key parameter.

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?

The description explains the tool's primary output and gives an analyte example, which is adequate for initial selection. However, the presence of an output schema mitigates the need to document return values, but the description still lacks details on how window_days affects results and whether any patient-specific constraints exist. Given the tool's moderate complexity, the description is minimally sufficient but not comprehensive.

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?

Schema description coverage is 0%, so the description must compensate. It provides concrete examples for analyte (scr_umol_L, k_mmol_L), which is helpful, but does not explain patient_id, window_days, or caller. The meaning of window_days (time range filter) is entirely undocumented, leaving the agent guessing. The description only partially covers parameter semantics.

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 states the tool provides a single-indicator time series with period-over-period change rate and 30-day slope, which clearly identifies its function. It distinguishes itself from siblings like get_labs by focusing on trend analysis rather than raw lab values. However, it lacks an explicit verb like 'retrieve' or 'list', though the intent is unambiguous.

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?

No explicit guidance is given on when to use this tool instead of siblings. The description implies it is for trend analysis, but it does not state when not to use it, prerequisites (e.g., lab data availability), or how it differs from get_labs or get_critical_values. The purpose is clear but the usage context is not.

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