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get_lab_trends

Retrieve chronological lab values for trend analysis in cancer care. Filter by parameter, date range, or limit results to visualize patient data changes over time.

Instructions

Retrieve stored lab values for trend analysis.

Returns values sorted chronologically (oldest first) for plotting trends.

Args: parameter: Filter by parameter name (e.g. PLT, SII, CEA). If None, returns all. date_from: Filter from this date (YYYY-MM-DD). date_to: Filter to this date (YYYY-MM-DD). limit: Maximum results to return.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parameterNo
date_fromNo
date_toNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that results are 'sorted chronologically (oldest first) for plotting trends,' which is useful behavioral context. However, it doesn't mention permissions, rate limits, error handling, or what happens with missing data—significant gaps for a tool with zero annotation coverage.

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 and appropriately sized. It front-loads the purpose, follows with behavioral context, and lists parameters clearly. Every sentence adds value, though the parameter explanations could be slightly more concise.

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 4 parameters with 0% schema coverage and no annotations, the description does a good job explaining parameter semantics and sorting behavior. Since an output schema exists, it doesn't need to detail return values. However, it lacks error handling or permission context, leaving minor gaps.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all 4 parameters: explaining that 'parameter' filters by name (e.g., 'PLT, SII, CEA'), 'date_from' and 'date_to' filter by date range in YYYY-MM-DD format, and 'limit' sets maximum results. This goes beyond the schema's basic types and defaults.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Retrieve stored lab values for trend analysis.' It specifies the verb ('retrieve'), resource ('stored lab values'), and context ('for trend analysis'). However, it doesn't explicitly differentiate from sibling tools like 'get_lab_summary' or 'get_lab_time_series', which appear related.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_lab_summary' or 'get_lab_time_series', nor does it specify prerequisites or exclusions. Usage is implied through parameter descriptions but not explicitly stated.

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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