geverifieerde_codeDe actuele kortingscode voor een merk in de gekozen markt, met laatst-geverifieerd-datum en succespercentage uit gebruikersstemmen.
| Name | Required | Description | Default |
|---|---|---|---|
| merk | Yes | ||
| 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, so the read-only nature is covered. The description adds useful behavioral context by disclosing that the result includes a last-verified date and a success percentage from user votes, which goes beyond the annotations.
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?
A single, front-loaded sentence that states the core resource first and then the distinguishing metadata. Every clause earns its place, and there is no redundant wording.
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 two-parameter read-only lookup with no output schema, the description covers the main return elements (code, verified date, success percentage) and the market context. It could mention what happens when no verified code exists, but that is a minor gap given the tool's simplicity and annotations.
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?
Schema description coverage is only 50% because 'merk' has no schema description. The description clarifies that 'merk' means the brand and relates 'markt' to the chosen market, but it does not specify format, required spelling, or behavior when no code is found. It provides modest added value but does not fully compensate for the missing schema coverage.
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 a specific resource ('kortingscode voor een merk'), a context dimension ('gekozen markt'), and distinctive output attributes (last-verified date and success percentage). This makes it clearly distinguishable from siblings like 'prijs_historie' and 'actuele_sales'.
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 description implies the tool is for retrieving a current verified discount code for a brand in a market, but it never explicitly states when to prefer this tool over alternatives. Sibling names are present in context, but the description does not reference them or provide exclusions.
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.