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Guardian Engine

by dev.kaimeilabs

get_master

Read-onlyIdempotent

Return the canonical master recipe for a dish (read-only, no LLM).

Enables compare-then-verify agentic loops: fetch the master, diff it against the user's recipe, then call verify_recipe — instead of verifying blind. Pure knowledge-base lookup, no LLM in the hot path.

Master content is transparent by default (ADR-009 / ADR-010): exact temperatures, timings, and EU FIC 1169/2011 allergen codes are returned verbatim, never obfuscated. No score is included (ADR-013) — this is reference data, not a verdict.

Returns ingredients, steps (technique/temperature/timing/medium), and the EU FIC allergens derived from the required ingredients. Unknown dishes return a structured UNKNOWN_DISH error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dish_nameNoName or alias of the dish to fetch the canonical master recipe for (e.g. 'carbonara', 'spaghetti bolognese', 'angel food cake'). Alias resolution and slug normalisation are applied. Use list_dishes() to browse.
response_formatNoResponse format: 'json' (default, structured) or 'text' (human-readable summary).json

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint, but the description adds rich behavioral context: no LLM in the hot path, transparent content per ADR-009/010, no score, and structured UNKNOWN_DISH error. This goes well beyond the annotation hints.

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 dense but every sentence earns its place: purpose, usage, transparency guarantees, return content, and error handling. Structured with line breaks for readability; appropriately sized for a tool with this complexity.

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?

Provides complete context: when to use, what it returns, what it doesn't return (score), and error behavior. Combined with annotations and output schema, the agent has everything needed to invoke 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 coverage is 100% with detailed descriptions for both parameters. The description adds some behavior (unknown dish error) but does not materially enhance parameter semantics beyond the schema. 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?

The description clearly states 'Return the canonical master recipe for a dish' with a specific verb and resource. It distinguishes the tool from siblings by positioning it as the reference lookup step before verify_recipe, contrasting with verification 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?

Explicitly describes the compare-then-verify agentic loop, directing the agent to fetch the master, diff, then call verify_recipe. It also clarifies that this is not for scoring (no verdict per ADR-013), preventing misuse.

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