produktdaten

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

With no annotations, the description carries the full burden, and it does meaningfully: it specifies the return contents (title, type, entry count, date, source URLs) and signals a public/no-auth read via 'des offenen ... Katalogs'. It stops short of stating safety hints or pagination behavior, but for a zero-parameter listing operation this is solid disclosure.

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?

Two tight sentences: the first front-loads the verb and resource plus return shape, the second supplies the routing hint. No filler or repetition.

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?

With no output schema and no parameters, the description compensates by describing exactly what each listed dataset entry contains, so the agent knows what it will get back without opening anything else.

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 tool takes zero parameters, so the baseline of 4 applies; there is no parameter semantics to explain and none are misdescribed.

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?

States a specific verb and resource ('Listet alle Datensätze des ... Produktdatenkatalogs auf') and enumerates the returned fields (Titel, Typ, Anzahl der Einträge, Stand, Quell-URLs). An agent can immediately tell this is the top-level enumeration tool rather than the search/detail/history siblings.

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 closing sentence gives clear usage context: 'Guter Einstiegspunkt, um verfügbare Kategorien und Datensatz-IDs zu ermitteln.' That tells the agent when to reach for this tool first, but it never names or excludes the alternatives (produkte_suchen, produkt_details, preis_verlauf), so no explicit when-not guidance.

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?

With no annotations, the description carries the full burden and does well: it discloses the history start date (2026-07-26), the per-product time-series shape, the all-products default, and the explicit caveat that live shop prices are authoritative. Auth, rate limits, and pagination are unaddressed, keeping it from a 5.

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 purpose and scope, with the field legend (d, p, pl, ph, pm) packed efficiently into parentheticals. Dense but every clause earns its place given the absence of an output schema; slightly hard to parse.

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?

No output schema exists, yet the description provides the return-field legend and scope, which is exactly what an agent needs to interpret results. Complete for a read tool, with minor gaps around pagination and access constraints.

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 100%, so baseline is 3, but the description adds meaning beyond the schema by explaining the default behavior when the optional produkt_id is omitted (history of all products in the dataset). That is a genuine semantic addition over the schema text.

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?

States a specific verb+resource: it delivers the editorially-observed price history (Preis-Radar) as a per-product time series. It explicitly distinguishes itself from a live price comparison and clarifies scope, so an agent can tell it apart from siblings like produkt_details.

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 gives a useful when-not caveat ("Kein Live-Preisvergleich — maßgeblich ist der Preis im Shop") and describes the no-produkt_id fallback, implying usage. However it never names or routes to the sibling alternatives (produkte_suchen, produkt_details) or states prerequisites, so guidance remains implied rather than explicit.

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?

With no annotations, the description must carry the behavioral burden. It usefully discloses what the response contains ('alle Attribute, Preismodell, Bezugsquellen, Quelle, Felddefinitionen') including license and citation info, and 'Liefert' implies a read-only operation. However, it does not explicitly state read-only status, authentication needs, rate limits, or side effects.

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 leads with the core purpose and then enumerates the returned contents. There is no filler or redundancy, though the parenthetical inventory makes it slightly dense.

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 two-parameter read tool with no output schema, the description adequately explains the return contents, which is the main gap an output schema would otherwise fill. The main omission is sibling differentiation and usage context, which is covered (or not) elsewhere.

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 description adds no meaning for the two parameters, but schema description coverage is 100% and both parameters (datensatz, produkt_id) are documented with examples in the schema. Baseline 3 applies when the schema fully documents inputs.

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?

States a specific verb ('Liefert') and resource ('vollständigen Datensatz-Eintrag eines Produkts') and enumerates the contents returned (attributes, pricing model, sources, license/citation). It is clear what the tool does, but it does not explicitly differentiate itself from siblings like produkte_suchen or preis_verlauf.

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 provides no guidance on when to use this tool versus produkte_suchen, preis_verlauf, or datensaetze_auflisten. It does not state prerequisites or alternative conditions, leaving usage inference entirely to the agent.

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?

With no annotations, the description carries the full burden and does disclose meaningful behavior: results are 'kompakt', include affiliate/Bezugsquellen links explicitly marked as sponsored (monetization disclosed), and require suchbegriff or datensatz. It omits auth/permission needs and pagination behavior, but for a read-only search the disclosure level is solid.

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?

Three short sentences: what it does, what comes back, and the input constraint. Front-loaded with the action and no filler; every sentence carries distinct information.

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?

With no output schema, the description compensates by enumerating the return payload (price, attributes, editorial assessment, sources, attribution). The precondition covers the 0-required-parameter schema gap. Only minor gaps remain, e.g. behavior when limits are exceeded or the 25-cap interaction with pagination.

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 100%, so the baseline is 3; the description adds value beyond the schema by stating the cross-parameter constraint that at least suchbegriff or datensatz is required, which is not expressed anywhere in the schema (0 required parameters).

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?

States a specific verb (Durchsucht) and resource (Produktdatenbanken) and clarifies the two scopes: catalog-wide or within a single Datensatz. It does not differentiate itself from siblings like produkt_details or preis_verlauf, but the search-plus-filter scope 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?

The sentence 'Mindestens suchbegriff oder datensatz angeben' gives a hard precondition, which is real guidance. However, there is no when-to-use/when-not framing, and no routing to siblings such as produkt_details (for full detail) or preis_verlauf (for price history), leaving that inference to the agent.

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