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Wellness Project MCP

Wellness Project MCP

by turnnoblindeye
OverviewSchemaSourceRelated ServersDiscussions(2)Admin
Python
Remote

list_lab_markers

Read-onlyIdempotent

Retrieve all LOINC-coded lab markers with canonical names, codes, panels, units, and aliases to match user-provided lab result names before logging, preventing naming inconsistencies.

Instructions

Returns all LOINC-coded markers in the reference library: canonical name, LOINC code, panel, typical unit, and common aliases. Call this BEFORE log_lab_results to match user-provided marker names to canonical entries — same pattern as list_exercises for workouts. Prevents name drift and ensures trending works across lab visits.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_lab_markers"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYesHuman-readable result text returned by the tool.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.1

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered structurally. The description adds useful behavioral context: it returns canonical library data, includes aliases for matching, and supports canonicalization. No contradiction with 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?

Three sentences with no filler. The primary return value is front-loaded, the output fields are enumerated compactly, and the usage directive follows naturally. Every sentence 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?

The tool has no parameters, has an output schema, and is fully annotated as read-only and idempotent. The description adds the only missing context: what the data represents, what fields are returned, and how it should be used in the logging workflow. Nothing essential is left out.

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?

There are zero parameters, so there is no parameter burden for the description to carry. The description instead clarifies the semantic meaning of the returned canonical marker set, which is more valuable here. Baseline 4 is appropriate for a no-parameter tool.

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 ('Returns'), a specific resource ('LOINC-coded markers in the reference library'), and enumerates the returned fields. It clearly distinguishes itself from list_lab_results, which is about logged lab results rather than the reference marker library.

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

Usage Guidelines5/5

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

The description explicitly tells the agent to call this BEFORE log_lab_results, gives the matching purpose, references the analogous list_exercises pattern, and explains why it matters ('prevents name drift and ensures trending works'). This is strong when-to-use guidance with a concrete alternative pattern.

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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