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Get lab test

vital_get_lab_test
Read-only

Get a single lab test by id. Vital API: GET /v3/lab_tests/{lab_test_id}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lab_test_idYesThe lab test id (UUID).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, and the description aligns by saying 'Get' and showing the HTTP GET method. It adds the concrete API path but does not disclose additional behavioral details such as error handling, authentication needs, or response shape; the read-only safety profile is already covered by annotations.

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 two short sentences with no filler: the core action is stated first, and the API endpoint is provided as useful implementation detail. Every word earns its place.

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

Completeness5/5

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

For a simple read-only getter with a single required parameter, this description is complete. The schema documents the parameter, the annotation confirms read-only behavior, and the endpoint gives the exact API contract. No additional context is needed for correct invocation.

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?

Schema description coverage is 100%, with lab_test_id fully described as 'The lab test id (UUID).' The description's endpoint template repeats the parameter but doesn't add new semantic meaning beyond what the schema already provides, so the baseline of 3 applies.

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 states a specific verb and resource: 'Get a single lab test by id.' It clearly distinguishes this from sibling list/get operations by emphasizing 'single' and 'by id,' and it includes the exact API endpoint.

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

Usage Guidelines4/5

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

The phrase 'single lab test by id' gives clear context for when this tool is appropriate: use it when you have a specific lab_test_id and need that one record. It does not explicitly name alternatives or state when not to use it, but the scope is apparent even without opening the schema.

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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TDQS

A3.6/5.0
Disambiguation5/5

Each tool maps to a unique resource/action pairing—users, health summaries, timeseries, providers, and lab orders—so an agent can reliably distinguish them. Even similarly named getters are separated by the data domain (activity/body/sleep/workouts) and description.

Naming Consistency4/5

All tools use the vital_ prefix and snake_case verb_noun forms, which is highly predictable. Minor inconsistency: get is used for both single-resource fetches and list-returning calls (get_workouts, get_user_connected_providers) while list is reserved for global collections.

Tool Count4/5

21 tools is on the heavier side, but the breadth of the Vital API—users, providers, many health summary types, timeseries, and lab tests/orders—justifies most of them. It is slightly over a typical focused MCP server but not bloated or redundant.

Completeness3/5

The read side is strong: users, providers, summaries, timeseries, lab tests, and results are all covered. However, there are no update/delete user operations and no way to create a lab-test order, so core lifecycle/workflow gaps remain.