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foodose GLP-1 nutrition

Appetite suppression by drug and dose

get_appetite_ceiling
Read-onlyIdempotent

The appetite suppression factor foodose models for a given medication and dose, with the evidence it is anchored to and the licensed doses for that drug. This is the site's original data: the estimate of how much less a person eats at a dose, rather than how much weight they lose. Call with no medication to get the whole table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doseMgNo
medicationNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context: it returns the factor, the supporting evidence, licensed doses, and the whole-table behavior when medication is omitted. It does not describe the output shape, but this is a simple read-only lookup.

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 compact and front-loaded with core semantics, then ends with a practical usage tip. Minor awkwardness around 'foodose models' and the phrase 'This is the site's original data' adds context without being bloated.

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?

For a two-parameter, read-only tool with no output schema, the description explains what is returned, the semantic distinction from weight loss, and how to retrieve the full dataset. It could be more explicit about the response format, but nothing critical is missing for correct tool selection.

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 carries the burden of explaining the parameters. It explains that medication and dose select the factor, and that omitting medication returns the full table. It doesn't spell out doseMg units explicitly, though the property name and title make the meaning clear enough.

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 resource and purpose: returning a foodose appetite suppression factor for a medication and dose, along with evidence and licensed doses. It also distinguishes this from weight-loss estimates ('rather than how much weight they lose'), which separates it from sibling calculate_glp1_target.

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?

It gives clear usage context: call with a specific medication and dose, or call with no medication to get the whole table. It does not explicitly name alternatives like calculate_glp1_target or search_foods, but the context is specific enough to avoid confusion.

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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