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iNutriPlan Supplement Database MCP Server

get_ingredient_info

Look up evidence-graded research context for a supplement ingredient from the curated registry (99 ingredients).

Returns canonical name, aliases, health goals, mechanisms of action, evidence grade (Strong / Moderate / Preliminary), research notes, and recommended iHerb search terms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesIngredient name, alias, or health goal keyword, e.g. "magnesium", "ashwagandha", "sleep", "testosterone", "weight_loss".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Discloses return fields and notes the registry size (99 ingredients). With no annotations, it provides moderate transparency about what the tool does, but lacks information on error handling (e.g., unknown ingredient) or any side effects.

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?

Two sentences cover action, source, and return fields efficiently. Front-loaded with the primary purpose and no redundant information. Every sentence earns its place.

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?

Well-suited for a simple lookup tool with one parameter and an output schema. Lists returned fields and registry scope. Could be improved by noting possible errors (e.g., ingredient not found), but overall sufficiently complete.

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% and already explains the parameter well (name, alias, or health goal keyword). The tool description reinforces the purpose but adds little beyond what the schema provides, so baseline score is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it looks up evidence-graded research context for supplement ingredients from a curated registry. Lists return fields. However, it does not explicitly differentiate from sibling tools like get_supplement_detail or search_by_ingredient, which could cause ambiguity.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. The description implies use for ingredients, but there is no mention of when not to use it or which sibling tool to choose for different scenarios.

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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