score_methodologieHet volledige SupplementScore-scoremodel, machine-leesbaar: criteria, letterschaal, bewijslast, onafhankelijke review en dispuutprocedure.
| Name | Required | Description | Default |
|---|---|---|---|
| markt | No | Markt/land voor prijzen, links en codes (standaard nl). | nl |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by stating the output is machine-readable and complete, and by listing the content components an agent can expect, though it stops short of specifying the exact machine-readable format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence that front-loads the core object and then lists its main components. There is no filler or repetition of information already fully covered by the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with no output schema, the description communicates the main return contents well. It is slightly incomplete because it does not elaborate on the exact machine-readable structure or how the 'markt' parameter alters the output, but those details are at least partially covered by the parameter schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully documents the single 'markt' parameter with an enum, default value, and a descriptive explanation, so the baseline is 3. The description adds no further meaning about how the market choice affects the returned methodology.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource as the complete SupplementScore scoring model and enumerates its machine-readable contents: criteria, letter scale, burden of proof, independent review, and dispute procedure. This clearly communicates what the tool provides and distinguishes it from product/sales-oriented sibling tools, though it lacks an explicit verb such as 'returns' or 'retrieves'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose implies this tool should be used when the scoring methodology itself is needed, as opposed to product scores, sales data, or deals. However, the description gives no explicit when-to-use or when-not-to-use guidance, nor does it reference any alternative sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct concerns: current sales, expected sales, weekly deals, price history, product reviews, discount codes, and search are clearly separable. The main risk is that actuele_sales, verwachte_sales, and week_deals all deal with 'sales/deals', but their temporal and channel scopes are explicit enough to prevent serious misselection.
All tool names use lowercase snake_case and are Dutch descriptive phrases, which gives a coherent feel. The pattern is not strictly verb_noun because most names are noun phrases like product_oordeel and prijs_historie, with zoek_producten as the only verb-led exception, so the set is consistent but not perfectly uniform.
Nine tools is well within the ideal range for a supplement deals/reviews domain. Each tool covers a distinct information need without obvious redundancy or bloat.
The set covers the full user journey for supplement research: searching products, checking current deals, anticipating future sales, validating discounts with price history, retrieving discount codes, reading quality scores, and understanding the scoring methodology. No significant operational gap is apparent for the stated domain.