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Glama

Guardian Engine

by dev.kaimeilabs

verify_dietary_claim

Read-onlyIdempotent

Verify that a recipe satisfies a dietary claim (vegan, halal, gluten-free, ...).

Reuses the existing allergen-detection logic plus a curated forbidden-ingredient map (apps/guardian/knowledge/dietary_claims.yaml). Returns a structured verdict with the specific offending ingredients and a short justification — never a vague paraphrase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimNoDietary claim to verify: vegan | vegetarian | gluten_free | dairy_free | nut_free | halal | kosher.
candidate_jsonNoRecipe JSON string (CandidateRecipe schema). Expected shape: {"title": "...", "ingredients": [{"name": "..."}, ...], "steps": [...]}. Only the ingredient list is required for dietary verification.
response_formatNoResponse format: 'text' (default, human-readable) or 'json' (machine-actionable).text

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses that verification combines existing allergen logic with a curated YAML map, and promises a structured verdict with offending ingredients and a short justification, adding strong behavioral specificity.

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 tight sentences lead with the core action, then add implementation and output details; no filler.

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 read-only verification tool with annotations and an output schema, the description covers purpose, mechanism, and result shape. It lacks only explicit sibling differentiation, but that is already captured by the tool's naming and usage guidelines dimension.

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 coverage is 100%, so the baseline is 3. The description itself does not elaborate on claim, candidate_json, or response_format; those are already described in the input schema, and the tool description adds no param-specific information.

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 opens with a specific verb and resource—'Verify that a recipe satisfies a dietary claim'—and enumerates claim types (vegan, halal, gluten-free), clearly distinguishing it from sibling check_allergens by focusing on dietary labels rather than allergen presence.

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 context: this tool verifies dietary claims and reuses allergen-detection logic, implying a relationship to check_allergens. It does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

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

A4.4/5.0
Disambiguation3/5

check_allergens, check_safety, and verify_dietary_claim all involve allergen scanning, so an agent could plausibly select the wrong one depending on whether it needs an ingredient audit, a master-independent safety envelope, or a dietary claim. The descriptions contain helpful usage hints, but the boundaries between the allergen-focused checks are not crisply defined.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun convention: check_allergens, check_safety, fix_recipe, get_master, list_dishes, verify_dietary_claim, verify_recipe. There is no mixing of casing styles or vague generic verb naming.

Tool Count5/5

Seven tools is well-scoped for a recipe verification engine: discovery, reference retrieval, verification, repair, and independent safety checks each have a dedicated entry point. No tool feels redundant or unnecessary, and the set is small enough for an agent to navigate easily.

Completeness4/5

The core workflow is covered end-to-end: list_dishes and get_master enable discovery and reference comparison, verify_recipe and fix_recipe handle master-based verification and repair, and check_safety, check_allergens, and verify_dietary_claim cover independent safety checks. Minor gaps exist, such as the lack of master-authoring/update tools and master-independent temperature safety being limited to poultry, but agents can work around these.

Resources