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Guardian Engine

by dev.kaimeilabs

check_safety

Read-onlyIdempotent

Run master-independent safety checks on a candidate recipe.

Works for ANY recipe — no dish resolution, no master SOP required. Checks poultry internal-temperature safety and scans all ingredients for the 14 EU FIC 1169/2011 Annex II allergen groups. The verdict is a deterministic function of (candidate, kb_version_hash) — no LLM involvement.

Use this when verify_recipe has no matching master for the dish: the safety layer still applies to every recipe.

Returns: Safety envelope: verdict (PASSED/FAILED per the zero-critical policy gate), safe flag, issues found, and the pinned kb_version_hash.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
candidate_jsonYesThe full candidate recipe as a JSON string. Checked for poultry internal temperature safety (≥74°C) and EU FIC 1169 allergen presence.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

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

The description discloses behavior beyond the readOnlyHint and idempotentHint annotations: the verdict is 'a deterministic function of (candidate, kb_version_hash) — no LLM involvement' and the checks are precisely enumerated (poultry ≥74°C, 14 allergen groups). It also details the return envelope components, adding valuable transparency about outputs.

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?

The description is well-structured: a lead sentence, a purpose statement, a usage note, and a brief return summary. Each sentence contributes meaning, though some redundancy exists between the first paragraph and the schema description. It is not overly verbose and front-loads the core purpose.

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?

Given the tool's complexity and the presence of an output schema, the description is complete: it explains what the tool does, when to use it, that it is deterministic and read-only, and what the return envelope contains. No major gaps remain for an agent 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?

The schema already covers the sole parameter (candidate_json) with 100% description coverage, including the checks performed. The description reiterates these semantics without adding new parameter-level details. Since the schema does the heavy lifting, the baseline of 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 clearly states the tool's function: 'Run master-independent safety checks on a candidate recipe.' It specifies the exact checks (poultry temperature and 14 EU FIC allergen groups) and differentiates from siblings by emphasizing 'no dish resolution, no master SOP required' and explicitly contrasting with verify_recipe when no master exists.

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?

Direct usage guidance is provided: 'Use this when verify_recipe has no matching master for the dish: the safety layer still applies to every recipe.' This clarifies the intended context and distinguishes it from the alternative verify_recipe. The phrase 'Works for ANY recipe' also sets boundary conditions.

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