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loinc_search

Read-onlyIdempotent

Search by term or partial code to retrieve LOINC codes for laboratory tests, clinical observations, and measurements.

Instructions

Search for laboratory tests, clinical observations, and measurements in LOINC (Logical Observation Identifiers Names and Codes).

Use this tool to:

  • Find LOINC codes for lab tests (e.g., "glucose", "hemoglobin")

  • Search for clinical measurements and vital signs

  • Look up diagnostic observations

Returns matching LOINC codes with names, components, and properties.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch term (test name, keyword, or partial LOINC code)
max_resultsNoMaximum number of results (1-100). Default: 25

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
total_countYes
shown_countYes
itemsYes
Behavior3/5

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

The annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, so the safety profile is clear. The description adds that the tool returns matching LOINC codes with names, components, and properties, which is helpful but not extensive. No additional behavioral traits (e.g., ordering, pagination, performance) are disclosed.

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 relatively concise with four sentences: an opening sentence, a bulleted list of use cases, and a closing sentence on return values. It is front-loaded with the main action and uses a clear structure. Minor redundancy could be trimmed, but overall it is well-organized and efficient.

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 simplicity (search with two parameters), the description is reasonably complete. It covers the domain, usage examples, and return format. An output schema exists, so return values need not be exhaustively described. However, it might miss details like search behavior (exact vs. fuzzy matching) or whether partial LOINC codes are accepted, but these are minor gaps.

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

Parameters3/5

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

Both parameters have schema descriptions, resulting in 100% coverage. The tool description does not add significant new information beyond the schema: it repeats the search term concept and examples. The max_results parameter is already described clearly in the schema. Therefore, the description does not substantially enhance parameter semantics beyond the baseline.

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 searches for laboratory tests, clinical measurements, and observations in LOINC. It gives specific examples like 'glucose' and 'hemoglobin', and explains what is returned (codes with names, components, properties). This distinguishes it from siblings like loinc_details or loinc_panels, which are for retrieving details or panel members.

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 lists use cases (find LOINC codes, search for measurements, look up observations) but does not provide explicit guidance on when not to use this tool or recommend alternatives. For example, it does not mention that for specific code details or panel compositions, one should use loinc_details or loinc_panels. The usage context is implied but lacks exclusions.

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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