PrijsProfeet Deutschland

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral value beyond that by describing what each result type returns (offers per supermarket, six-month lowest price, cross-supermarket pricing) and that a page link is always included.

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?

Front-loaded with the core action, then the return variants, then the invariant (link). Dense but every clause adds distinct information; the semicolon-chained result types make it slightly heavier than necessary.

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 single-id fetch tool with an output schema present, the description is more than sufficient: action, id source, and result semantics per type are all covered. Only minor gaps remain (error/not-found behavior), which the output schema can reasonably absorb.

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 coverage is 0% for the single `id` parameter, so the description must carry the meaning. It does explain where the id comes from ('een resultaat van `search`') and hints at the id namespace via the `schap:` form, though it does not spell out id formats or validity rules.

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 gives a specific verb+resource ('Haal een resultaat van `search` op met zijn `id`') and explicitly ties itself to the sibling tool `search` as the source of the id. It then differentiates the return by result type (merk / product / schap:), so an agent can tell what this does versus search without opening a schema.

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?

It clearly establishes the workflow context: use this to retrieve a result whose `id` came from `search`. There is no explicit when-not-to-use statement or alternative routing, but the prerequisite (an id from search) is unambiguous.

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

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

Annotations already declare this a safe, idempotent read, but the description goes well beyond them: it describes what the response contains (brand page, per-supermarket prices, product results with size, promo price, pre-promo price, unit price), states that prices refresh nightly, and imposes a citation/URL obligation on the agent.

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?

Purpose, usage, and response shape are front-loaded and the language/translation advice is actionable. The language-to-domain mapping is stated twice (once for query language, once for connector country), which is some redundancy in an otherwise dense paragraph.

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 one-parameter search tool with an output schema, the description covers everything an agent needs: when to use it, what language to query in, which domain selects which country, what comes back, how fresh the data is, and how to cite it.

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 single `query` parameter has 0% schema description coverage, so the description must carry the meaning — and it does, explaining that queries should be words in the store's product-name language with translation examples ('peanut butter' → 'pindakaas'/'Erdnussbutter'). It stops short of describing formatting expectations such as multi-word or brand-only queries.

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 gives a specific verb and resource ('Zoek een product of merk bij de supermarkt op naam') with concrete example queries ('oatly barista', 'calvé pindakaas'), and it explicitly points to the sibling `fetch` for details, so the boundary between the two tools is unambiguous.

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?

It enumerates the exact question types this tool serves (prijs, aanbieding, goedkoopste supermarkt, beschikbaarheid) and directs detail lookups to `fetch`. It also adds usage conditions the agent could not infer, namely which connector domain maps to which country and which language to query in.

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