ZBS Index What actually exists in applied AI, with the source next to it

mcp server

Guardian Engine

Deterministic recipe verification engine — validates AI-generated recipes against master SOPs.

Description as published by the maintainer. Source

  • version 1.0.0
  • active

active — Most recent push to the repository was 2026-07-20.

What this server can do

7 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

check_allergens(dish_name, ingredients, restrictions, response_format, check_all_eu_allergens)
Check ingredients for EU FIC 1169/2011 allergen compliance. Returns a detailed audit trace mapping each ingredient to its EU Annex II allergen group with entry numbers and labels. The safety verdict is deterministic — no LLM involvement in the decision. Use check_all_eu_allergens=True for food labelling (detect all allergens). Use restrictions=['dairy', 'gluten'] to check for specific user allergies. Required: ingredients.
check_safety(candidate_json)
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. Required: candidate_json.
fix_recipe(dish, dish_name, master_json, candidate_json, original_prompt, response_format)
Deterministically repair a candidate recipe against a Guardian master. Verifies the candidate, applies every machine-actionable correction the symbolic engine produced (missing ingredients, quantities, temperatures, durations, cooking media, ingredient substitutions), then re-verifies the result. No LLM is used — the repair is a deterministic function of the candidate recipe and the master ruleset. Findings that need recipe-authoring judgement — adding a whole cooking phase, rewriting step instructions, ingredient-ratio rebalancing — are not auto-applied; they are returned under `patches_skipped`. Allergen findings are never auto-fixed. The response reports the verdict before and after so the caller can see exactly what was resolved. Note: `verdict_after` may still be FAILED when structural changes (e.g. adding a cooking step, rebalancing ingredient ratios) are needed. These require recipe-authoring judgement and are returned under `patches_skipped`. Callers should NOT assume a fixed recipe will pass verification.
get_master(dish_name, response_format)
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.
list_dishes(cuisine_filter)
List all available master dishes with rich metadata. Returns: Dictionary with `schema_version` and a `dishes` list. Each dish includes slug, title, cuisine, region, aliases, and complexity.
verify_dietary_claim(claim, candidate_json, response_format)
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.
verify_recipe(dish, dish_name, session_id, master_json, operator_id, candidate_json, original_prompt, response_format)
Verify a candidate recipe against a Guardian master recipe. Uses deterministic graph-based verification to check technique, temperature, timing, cooking medium, and required ingredients. **Verdict**: `verdict` is strictly PASSED or FAILED and is policy-driven — any CRITICAL finding fails the recipe; more than 5 WARNINGs also fail. There is no score in the response (ADR-013): gate on `verdict` and explain failures from `findings`. **Field audience**: `issue` is a machine-readable code for programmatic handling — never show it to end users. Use `title` and `suggested_correction` as the user-facing fields. Returns structured JSON by default (machine-actionable findings and patches); response_format="text" renders a human-readable report. Both formats are transparent (ADR-009 / ADR-018): exact values and ingredient names included.

Last successful function declaration observed on . Source: https://api.kaimeilabs.dev/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on . Source: https://api.kaimeilabs.dev/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Signal Value What it measures Window Observed Source
GitHub stars 0 Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. cumulative, all time GitHub
Last commit 2026-07-20 Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. point in time GitHub
Open issues 0 Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. as of fetch GitHub
Latest published version 1.0.0 Latest version string the maintainer published to the registry. as of fetch Model Context Protocol
Registry record last updated 2026-02-27 When the registry record was last updated by its maintainer. point in time Model Context Protocol
License MIT Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. as of fetch GitHub
First listed in the MCP Registry 2026-02-27 Date this server was first published to the official MCP Registry. Not a usage or quality measure. point in time Model Context Protocol
repository status active The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. as of fetch GitHub
mcp tools declared 7 tools Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. as of probe api.kaimeilabs.dev
mcp endpoint status ok The server listed 7 functions when asked. as of probe api.kaimeilabs.dev

Where to get it

This record as data

Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.

GET /api/v1/entries/mcp_server.json

Sources

  1. kaimeilabs/guardian-api-docs on GitHub — GitHub, observed , trust tier 3.
  2. Tools declared by the MCP server at https://api.kaimeilabs.dev/mcp — api.kaimeilabs.dev, observed , trust tier 1.
  3. Official MCP Registry — Model Context Protocol, observed , trust tier 1.