3Descu 3D Printing Quotes (EU)

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 real behavioral traits: the accepted file formats (.stl/.obj), the requirement that the URL serve the raw file rather than an HTML page, and the domain allowlist that constrains fetching. It omits auth requirements, size/rate limits, and error behavior, which keeps it short of 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?

Three tight sentences, each earning its place: what it does, the cross-tool handoff, and the fetch restriction. The core capability and output list are front-loaded with no 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 compensates by naming the exact measurements returned and even the field name surface_area_mm2, giving the agent enough to chain into estimate_price. The main remaining gap is output structure/units beyond surface area, but the essentials are covered for a single-parameter tool.

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?

Only one parameter exists and schema coverage is 100%, so the schema already documents the url parameter fully. The description reinforces the accepted formats and the raw-file requirement, but adds no syntax or constraint detail the schema lacks. Baseline 3 applies.

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 (download and measure) plus the exact resource (3D model from a public https URL) and enumerates the measurements returned: volume, bounding box, surface area, triangle count, watertightness. This is clearly distinguishable from estimate_price, get_capabilities, and list_materials.

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?

Gives explicit workflow guidance by naming the sibling to hand off to (feed surface_area_mm2 into estimate_price for an exact price instead of a range), which tells the agent why to pick this tool. It also states a hard boundary (only well-known model-hosting and file-sharing domains can be fetched), though it offers no other when-not conditions.

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 burden and does useful work: it discloses the pricing tiers quoted, that shipping and VAT are excluded, and that omitting surface area degrades the answer from a single price to a range. It doesn't address determinism, caching, or request limits, but for a pure estimation tool the disclosed boundaries are the material ones.

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 tight sentences: the core action first, the parameter-driven behavior second, the exclusions last. Nothing repeats the name or title, and every sentence changes what the agent knows.

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 must hint at return shape; it does so via 'the range the shape could produce' and the three production tiers, plus the shipping/VAT exclusions. Given nine parameters, a nested bounding_box object and required analysis-derived inputs, it is close to sufficient, though it never states the response structure or units beyond EUR.

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 schema already documents all nine parameters including the range-vs-single-price effect of surface_area_mm2 and its route to analyze_model. The description largely restates that schema content, adding no new syntax or defaults, so the baseline of 3 applies.

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 ('Price a part in EUR across economy, standard and express production') and pins the scope with 'using the same engine as the public calculator'. This is clearly distinguishable from siblings like analyze_model, list_materials and get_capabilities.

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?

Gives a concrete conditional for the optional parameter: supply surface_area_mm2 for a single price, omit it to get a range. It also flags shipping and VAT as excluded, which tells the agent when this tool's answer is or isn't the right one. It does not name a sibling alternative, but no obvious alternative pricing tool exists.

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 burden and does so reasonably: it discloses the full scope of returned information and frames the tool as a stateless first-step lookup. Nothing is said about response format or limitations, but for a zero-parameter read-only catalog call the disclosure is proportionate.

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 sentences, no filler: the content scope is front-loaded before the usage cue. Every clause maps to a distinct item the agent will receive.

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, so the description must convey return content, which the enumeration does well enough for an agent to know what it will learn. Minor gap: no hint at response structure or shape (single object vs. sectioned report).

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 is 4. No parameter meaning needed to be conveyed and none is claimed.

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 names the resource concretely and enumerates its contents (technologies, build volumes, layer heights, file formats, production tiers, minimum order value), so an agent knows exactly what it retrieves. It stops short of explicitly distinguishing itself from the sibling list_materials, which also surfaces material-related information.

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?

"Call this first if you do not know whether a part is feasible" gives a clear triggering condition and even a sequencing hint relative to analyze_model/estimate_price. It does not name alternatives for narrower needs (e.g., use list_materials for material specifics), so the routing is implicit rather than exhaustive.

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?

No annotations are provided, so the description carries the burden alone. It discloses the shape of the returned data (ids, density, suitability) and the dependency with estimate_price, which is genuinely useful, but says nothing about ordering, filtering behavior when technology is omitted, or caching for a catalog lookup.

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 short sentences, both front-loaded with the resource first and the consumer-facing return value second. Every clause earns its place with zero 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?

With no output schema and one optional parameter, the description correctly compensates by describing the returned fields and the id's purpose for estimate_price. It is nearly complete for a simple read-only catalog tool, missing only ordering/filter-default details.

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% with an enum on technology, so the schema already documents the single optional filter. The description's mention of FDM/SLA reinforces the enum values but adds no syntax or default behavior beyond the schema, matching the baseline of 3.

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 (list/returns) and resource (materials) with an explicit scope (FDM and SLA printing) and enumerates the payload fields (density, suitability). This makes it clearly distinguishable from analyze_model, estimate_price and get_capabilities without opening any 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?

The description implies a workflow by stating it returns the id that estimate_price expects, which hints at when to call it. However it never says when to prefer this over siblings like get_capabilities, and there are no exclusions or prerequisites stated outright.

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