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One substance, with everything its own roster says

get_substance
Read-only

One substance with everything its own roster says (same as HTTP GET /v1/substances/{id}): identifiers, category, aliases, translations, the source-ledger row, groups, and — depending on the axis — Japanese use standards, EU/Codex conditions of use, mandatory labelling statements, the roster's definition text or the CFR citation. Facts from the substance's own axis only; nothing here crosses an axis (that is get_substance_diff, paid, HTTP only). Axis-specific keys are present only where the roster has that concept, and an empty list means something (use_standards: [] = no use standard; use_conditions: [] = no permitted food category). Not a safety judgement: every record is a draft and needs verification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesSubstance id as returned by search_substances or analyze_label, e.g. eu:e951, jp:designated-aspartame, us:cfr-172-804.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds genuinely new semantics: axis-specific keys appear only where the roster has that concept, empty lists are meaningful ('use_standards: [] = no use standard'), and every record is a draft needing verification. This is substantive context beyond the safety profile.

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?

Front-loads the core resource statement and then layers scope, boundary, and caveats. The second and third paragraphs earn their place, though the long parenthetical key list is dense and slightly heavy for a single tool.

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?

There is no output schema, so the description must carry the return shape — and it does, enumerating the returned keys and the meaning of empty lists. Combined with the boundary and draft-status caveats, an agent has everything needed to call and interpret this tool.

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 the single id parameter is documented with format examples in the schema itself. The description adds no additional syntax or format detail beyond what the schema provides, so the baseline 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?

States a specific resource (one substance's full roster record) and enumerates the exact contents it returns (identifiers, category, aliases, translations, ledger row, groups, axis-specific fields). It explicitly carves out the sibling boundary: 'nothing here crosses an axis (that is get_substance_diff)'. An agent can distinguish this from get_substance_diff without opening either schema.

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?

Names the alternative (get_substance_diff) and the condition that selects it ('crosses an axis'), and the id schema points to search_substances/analyze_label as upstream sources. It stops short of saying when to prefer this over analyze_label, so it is clear but not fully exhaustive routing.

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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