frigolog-haccp-mcp

get_allergenes_reglementaires

Retourne la liste des 14 allergènes à déclaration obligatoire en France conformément au règlement UE 1169/2011 (INCO) applicable depuis le 13 décembre 2014 : gluten (blé, seigle, orge, avoine), crustacés, œufs, poissons, arachides, soja, lait (lactose), fruits à coque (8 types), céleri, moutarde, graines de sésame, anhydride sulfureux et sulfites, lupin, mollusques. Pour chaque allergène : noms communs, sources principales, sources cachées non évidentes, obligation d'affichage en restauration et sanction en cas d'omission.

[EN] Returns the 14 mandatory allergens under EU Regulation 1169/2011 (INCO): common names, main and hidden sources, display obligation, penalties. Optional 'allergene'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`allergene`	No	Filtre optionnel par allergène. Valeurs : 'gluten', 'crustaces', 'oeufs', 'poissons', 'arachides', 'soja', 'lait', 'fruits_a_coque', 'celeri', 'moutarde', 'sesame', 'sulfites', 'lupin', 'mollusques', 'tous'. Si absent ou 'tous', renvoie les 14 allergènes.

TDQS

A3.7/5.0

Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It thoroughly describes the output content (14 allergens, common/hidden sources, display obligations, penalties) and the optional filter behavior, exceeding what annotations would typically provide. It does not explicitly state side effects, but being a get tool, this is implied.

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 with the main action upfront, but it is somewhat lengthy due to listing all 14 allergens and includes a near-duplicate English summary. The information is relevant and earns its place, though the redundancy slightly reduces conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one optional parameter and no output schema, the description provides adequate detail about the return content and filter behavior. It could specify the exact output structure (e.g., list of objects), but the overall information is sufficient for an agent to understand the tool's functionality.

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 input schema already provides 100% coverage of the single optional 'allergene' parameter, including acceptable values and behavior when absent. The description only mentions 'Optional allergene' in the English summary, adding no additional meaning beyond the schema.

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 returns the list of 14 mandatory allergens in France, referencing specific regulations and providing detailed content. This specific verb+resource combination clearly distinguishes it from sibling tools that focus on HACCP, inspections, or temperatures.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide explicit guidance on when to use this tool versus alternatives. It implies its purpose through the content but lacks a statement like 'Use this when you need allergen declaration requirements' and offers no exclusions or alternative recommendations.

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

A3.9/5.0

Disambiguation4/5

Most tools have a clearly distinct purpose, but there are a few potentially confusable pairs: get_haccp_temperatures vs get_temperatures_cuisson, get_score_alimconfiance vs get_alimconfiance_etablissement, and get_rappels_produits_actifs vs get_rappels_par_categorie_etablissement. The descriptions explicitly cross-reference these, reducing ambiguity, but an agent could still misselect without careful reading.

Naming Consistency4/5

18 of 19 tools follow the consistent 'get_' + noun pattern (e.g., get_actions_correctives, get_seuils_microbiologiques). The lone exception is 'compare_solutions_haccp', which uses a different verb but still follows a verb_noun structure. This minor deviation keeps the set mostly predictable.

Tool Count3/5

With 19 tools, the count sits in the borderline-heavy range (16-25). The breadth is justified for a comprehensive HACCP regulatory assistant, but some tools like compare_solutions_haccp feel peripheral to the core compliance theme, making the set feel slightly overstuffed.

Completeness4/5

The set covers the HACCP domain extensively: temperatures, cooking, DLC, allergens, recalls, cleaning plans, checklists, corrective actions, DDPP documents, sanctions, training, and Alim'confiance. Minor gaps exist, such as no tool for generating a full HACCP plan document or tracking ongoing temperature logs, but for an informational/regulatory lookup server, coverage is strong.

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