dm-drogerie-markt

searchProducts

searchProducts

Read-onlyIdempotent

	Search for products available in the German dm-drogerie market (online and local stores).

	USE WHEN: searching dm-drogerie products by name, category, ingredient, property, or any
	natural language query (any language supported). Often answers questions about
	ingredients and properties directly. Covers: dm-drogerie markt brands, make-up,
	skincare, perfume, hair, health, nutrition, baby & child, household, home & living,
	photo, and pets.

	OUTPUT: Returns a maximum of 15 products. GTIN, DAN, brand, title, details, category,
	price, appLink (direct product URL), description, highlights/USPs, and extensive
	attributes including:
	- Dietary/Allergen: vegan, vegetarian, bio, glutenFree, lactoseFree, sugarFree,
	  nutFree, soyFree
	- Cosmetic Ingredients: fragranceFree, alcoholFree, parabenFree, sulfateFree,
	  preservativeFree, dyeFree, oilFree, siliconeFree, naturalCosmetics
	- Product Properties: waterproof, new, limitedEdition, sellout, onlineOnly,
	  exclusiveDm, dmBrand, purchasable

	NOT FOR: nutritional information (calories, protein, carbs, fats), complete allergen
	lists, full ingredient details. For these, use 'getProductDetails' tool with
	the GTINs or DANs.

	LIMITATIONS: Only make claims based on EXPLICITLY stated product highlights/descriptions.
	Do NOT extrapolate or assume properties not mentioned in the results.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	The search query for dm-drogerie products

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond that: a maximum of 15 products returned, detailed output structure, and a critical limitation instructing agents to only make claims based on explicitly stated product highlights and not extrapolate. This is significant and does not contradict annotations.

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 with clear sections (USE WHEN, OUTPUT, NOT FOR, LIMITATIONS), front-loaded with the core purpose, and every section earns its place. It is longer than average but appropriately so given the tool's complexity and absence of an output schema.

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?

There is no output schema, so the description must explain return values. It does this thoroughly, listing output fields (GTIN, DAN, brand, title, etc.) and extensive attribute categories. It also covers limitations and sibling-tool differentiation, making it highly complete for an agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% but the schema description for 'query' is generic. The tool description enriches the parameter meaning by specifying that the query accepts natural language, any language, and can target name, category, ingredient, property, etc. This goes beyond the schema's 'The search query for dm-drogerie products'.

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 states a clear verb+resource+scope: 'Search for products available in the German dm-drogerie market.' It also distinguishes from the sibling tool getProductDetails by explicitly stating what it is NOT for and pointing to the alternative, making the purpose unmistakable.

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 provides explicit 'USE WHEN' conditions (searching by name, category, ingredient, property, or natural language) and 'NOT FOR' exclusions (nutritional info, complete allergen lists, full ingredient details) with a direct pointer to getProductDetails. This fully covers when to use versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All dm-drogerie-markt tools Inspect

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TDQS

A4.7/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: findNearbyStores for location-based store search, searchProducts for finding products by query, and getProductDetails for retrieving detailed info by ID. No overlap in their core functions, and the descriptions explicitly clarify when to use which.

Naming Consistency5/5

All tool names follow a consistent verb_noun camelCase pattern: findNearbyStores, getProductDetails, searchProducts. The naming style is uniform and predictable.

Tool Count5/5

With 3 tools, the server is well-scoped for its apparent purpose of store locator and product information. Each tool covers a distinct need without redundancy or bloat, fitting comfortably within the ideal 3-15 range.

Completeness4/5

The core workflows are covered: finding nearby stores, searching products, and getting detailed product data. Minor gaps exist, such as lacking a direct store-by-ID lookup, but these can be worked around using existing tools and do not cause dead ends.

Resources

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