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get_lab_time_series

Retrieve chronological lab parameter data with reference ranges, units, and computed deltas to build trend charts and analyze cancer patient progress over time.

Instructions

Get structured time series data for one or more lab parameters.

Returns chronological values with reference ranges, units, and computed deltas (absolute change and % change between consecutive measurements). Designed for Oncoteam and MCP clients to build trend charts and analysis.

Args: parameters: Comma-separated parameter names (e.g. "CEA,CA19_9" or "PLT"). date_from: Start date filter (YYYY-MM-DD). Optional. date_to: End date filter (YYYY-MM-DD). Optional.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parametersYes
date_fromNo
date_toNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 the tool returns chronological values with reference ranges, units, and computed deltas, which adds behavioral context beyond basic retrieval. However, it lacks details on permissions, rate limits, or error handling that would be important for a data-fetching tool.

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

Conciseness5/5

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

The description is well-structured and front-loaded, starting with the core purpose, followed by return details, usage context, and parameter explanations in a bullet-like format. Every sentence adds value without redundancy.

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 tool's complexity (3 parameters, no annotations, but with an output schema), the description is fairly complete. It explains the purpose, return data structure, and parameter usage, though it could benefit from more behavioral details like data source or limitations, which the output schema might cover.

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 meaning by explaining that 'parameters' are comma-separated names with examples (e.g., 'CEA,CA19_9'), and clarifies that date filters are optional with format 'YYYY-MM-DD', which is not evident from the schema alone.

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 states the tool's purpose with specific verbs ('Get structured time series data') and resources ('lab parameters'), distinguishing it from siblings like get_lab_summary or get_lab_trends by focusing on chronological values with computed deltas for trend analysis.

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

Usage Guidelines3/5

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

The description implies usage for building trend charts and analysis, targeting Oncoteam and MCP clients, but does not explicitly state when to use this tool versus alternatives like get_lab_summary or get_lab_trends, nor does it provide exclusions or prerequisites.

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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