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Guardian Engine

by dev.kaimeilabs

list_dishes

Read-onlyIdempotent

List all available master dishes with rich metadata.

This is a browse/discovery step, not the verification itself — after picking a dish, call verify_recipe(dish_name=, candidate_json=) to actually check a candidate against it (or fix_recipe to auto-repair it).

Returns: Dictionary with schema_version, a dishes list (slug, title, cuisine, region, aliases, complexity per dish), and a next_step hint describing how to proceed to verification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cuisine_filterNoOptional cuisine to filter by. Case-insensitive exact match against the dish's cuisine field. Valid values: italian | french | spanish | british | thai | chinese | indian | indonesian | japanese | malaysian | korean | mexican | american | moroccan | turkish | levantine. Leave empty to return all available dishes.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark this as read-only and idempotent, so no side-effect warning is needed. The description adds useful behavioral context beyond annotations by describing the return payload (schema_version, dishes list, next_step hint) and framing the tool as a discovery step rather than the verification action itself.

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?

The description is well structured: a one-line summary, a concise usage directive with named alternatives, and a compact returns overview. Every sentence adds value, and the most important routing guidance is front-loaded.

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 simple optional-parameter listing tool with a rich output schema and read-only/idempotent annotations, the description is complete. It explains when to use it, what it returns at a high level, and how to proceed to verification, so an agent has enough context to invoke it correctly.

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 input schema already documents cuisine_filter clearly, including its default, case-insensitive exact-match behavior, valid values, and how to return all dishes. The description adds no extra parameter-level meaning, so the baseline 3 is appropriate.

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-resource pair: 'List all available master dishes with rich metadata.' It clearly distinguishes itself from verification tools by stating it is 'a browse/discovery step, not the verification itself' and explicitly names verify_recipe and fix_recipe as the follow-up tools.

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 gives explicit usage context: use this to browse or discover dishes before verification, then call verify_recipe or fix_recipe afterward. This directly tells the agent when to use this tool and what to use instead for the actual verification step, reducing ambiguity against siblings.

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.

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