drukarnia

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

No annotations are provided, so the description must carry the burden. It explains what the tool returns and how width_mm/height_mm affect pixel dimensions, but it does not explicitly state that this is a read-only operation, mention authentication prerequisites, or address invalid inputs. This is a partial 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 sentences, front-loaded with the primary purpose, and the second sentence adds a specific use case (AI image generation) without redundancy. Every word earns its place.

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?

The description covers the main purpose, a special feature (ai_generation), and parameter behavior. Since an output schema exists, return values need not be detailed. Minor gaps include prerequisites and error conditions, but overall it's sufficient for an AI agent to decide invocation.

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 0%, so the description must compensate. It explains width_mm and height_mm as trim size in millimeters used to compute custom pixel dimensions, which adds value. However, product_slug is not explicitly described, though it is implied by 'barwa.pl product'. Partial compensation.

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 states the verb 'Get' and the resource 'print-file preparation requirements for a barwa.pl product', specifying format, color mode, resolution, bleed. This distinguishes it from sibling tools like get_quote or list_products.

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 gives clear context for when to use the tool — when print-file specs or AI-generator pixel dimensions are needed. It doesn't explicitly exclude alternatives, but the sibling tools have distinct purposes, so the context is sufficient.

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 full burden. It describes that the tool returns a full set of quotable parameters, including mandatory finishes. It is transparent about the output without mentioning side effects or auth, which is acceptable for a schema retrieval tool.

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?

The description is two sentences, with the main purpose upfront ('Get the full set...') followed by specific details. Every sentence adds value, no fluff.

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?

Given one input parameter and an output schema (not shown), the description covers what the tool returns and when to use it. It could be improved by mentioning that product_slug should be a valid slug, but for a schema retrieval, it is mostly complete.

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 parameter product_slug is not described in the schema (0% coverage) and the description only indirectly implies it's a product identifier. It does not give examples or format, but the tool name and purpose provide enough context for a single parameter. Baseline 3 for single param with no schema description.

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 states the verb 'get' and the resource 'product pricing schema'. It lists specific contents like sizes, quantity limits, materials, etc., and distinguishes from sibling tools by noting it should be called before get_quote, differentiating it from get_quote, get_file_specs, and others.

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 explicitly says 'Call before get_quote to know valid option ids', providing clear usage context. It implies when to use it but does not explicitly mention when not to use it or list alternatives, but the context from siblings is sufficient.

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 full burden. It discloses that the price matches the on-site calculator (accuracy guarantee) and lists return elements (net/gross price, currency, delivery time, product name/link). However, many parameters (pages, finishes, material_id, etc.) remain unexplained, leaving behavioral gaps.

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?

The description is brief (three sentences) and front-loaded with purpose. Information about equivalence, returns, prerequisites, and dimensions is logically ordered. One could argue it could be more structured, but it is efficient and avoids redundancy.

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?

Given the presence of an output schema and sibling tool references, the description covers core aspects: purpose, return values, and a key prerequisite. However, it omits details on half the parameters and does not address potential side effects or limitations, making it only moderately complete for a tool with 10 parameters.

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 0%, so the description must compensate. It adds meaning for width/height ('in mm (free-size) or format_id (predefined)') but does not explain pages, finishes, material_id, double_sided, or liczba_wzorow. This partial coverage provides some value but leaves 8 out of 10 parameters undocumented.

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 states the tool's purpose: 'Get a price quote for a barwa.pl product.' It specifies the verb 'get', the resource 'price quote', and distinguishes itself from siblings like 'get_product_pricing_schema' (which provides schema, not a quote) and 'get_shipping_info'.

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 explicitly advises to use 'get_product_pricing_schema first for valid option ids,' providing clear dependency guidance. It does not explicitly state when not to use the tool or compare to alternatives, but the context is clear enough for appropriate usage.

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 present, but the description clearly states what data is returned (thresholds and free-shipping with context). For a read-only tool with no side effects, this is sufficient transparency.

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?

The description is a single, clear sentence with no unnecessary words. It is perfectly concise and front-loaded.

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 parameters and an existing output schema (implied), the description fully specifies what the tool returns. No additional context is needed given the tool's simplicity.

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?

There are zero parameters, so the baseline is 4. The description adds value by explaining the output, which goes beyond the empty input schema.

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 explicitly states it retrieves shipping cost thresholds based on order weight and the free-shipping threshold, all in the shop's country and currency. This is specific and distinct from sibling tools which deal with files, pricing, quotes, products, and recommendations.

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?

No explicit usage guidance or alternatives are provided. However, the tool's purpose is straightforward with no parameters, making when to use it self-evident. The lack of explicit direction keeps this at a 3.

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 provided; the description describes a simple list operation with no destructive actions. It could be improved by stating read-only or authentication requirements, but the current description is adequate.

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, front-loaded with purpose, no extraneous words. Every sentence adds value.

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 parameterless list tool with an output schema, the description covers the purpose and cross-references the pricing tool, making it complete.

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?

No parameters exist, so the baseline is 4. The description adds no parameter info, which is acceptable since there are none.

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 states the tool lists printing products from a specific shop, with examples, and distinguishes from sibling tools like get_quote which handles pricing.

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?

Explicitly mentions when to use this tool (to list products) and directs the agent to use get_quote for pricing, including the required slug parameter.

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 full burden. It discloses that the tool returns products with links and directs pricing to get_quote. It also instructs on handling empty results. For a recommendation tool, this sufficiently covers behavioral traits.

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?

The description is extremely concise with two sentences. It front-loads the core purpose and adds necessary guidance without any wasted words.

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?

Given the presence of an output schema (not shown), the description does not need to detail return values. It covers the essential behavior: recommending products, mentioning links, and referencing pricing. The instruction for empty results adds completeness. The tool is simple enough that this is adequate.

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 parameter 'purpose' has no description in the schema (0% coverage). The description adds meaning by providing examples of needs ('trade fair', 'gift packaging') and implies it is a free-text customer need. This adequately compensates for the schema gap.

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 uses a specific verb 'Recommend' and clearly identifies the resource 'barwa.pl products'. It provides concrete examples ('something for a trade fair', 'gift packaging') that distinguish it from sibling tools like list_products or get_quote.

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 states when to use the tool (to match a customer need) and what to do if no results (ask for clarification). It also references a related tool (get_quote) for pricing. However, it does not explicitly exclude scenarios or mention alternatives.

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