prijs_historiePrijsverloop van een product (dagmetingen): laagste/hoogste/mediaan en of de huidige 'korting' echt een korting is.
| Name | Required | Description | Default |
|---|---|---|---|
| dagen | No | ||
| markt | No | Markt/land voor prijzen, links en codes (standaard nl). | nl |
| product_key | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral detail beyond the readOnlyHint annotation: it states the data is daily measurements and that the tool evaluates whether a current discount is genuine. This gives the agent insight into the analysis performed, not just the fact that it is a read operation.
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, information-dense sentence. It front-loads the core purpose and adds the distinguishing discount check without any redundant filler.
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?
There is no output schema, so the description carries responsibility for explaining return semantics; it does so at a high level (low/high/median, discount check). However, it omits how the optional parameters like dagen and markt affect results and does not clarify the exact expected response format.
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?
With only 33% schema description coverage, the description should compensate for undocumented parameters, but it does not. product_key is not explained, and dagen is not connected to the 'dagmetingen' concept. Only markt has a schema description; the tool description adds no parameter-level clarity.
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 clearly identifies the tool as providing price history for a product with daily measurements and specific statistics (low/high/median) plus a discount-authenticity check. It distinguishes this tool from sales-focused siblings by its price-history focus, though it lacks an explicit verb like 'retrieve' or 'get'.
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?
Usage context is implied: an agent would use this when price history or discount legitimacy is needed. However, there is no explicit guidance on when to prefer this tool over alternatives such as actuele_sales or week_deals, nor any when-not-to-use conditions.
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.