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

Annotations are neutral (all false), so description carries burden. It discloses that the tool saves and returns data, but does not explain side effects like image uploading or storage lifetime. Some behavioral context is added but not comprehensive.

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 sentence that is efficient and begins with the verb and resource. It conveys key information without redundancy, though it could be slightly more streamlined.

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 5 parameters, no output schema, and complex nested objects, the description gives a high-level understanding of behavior and return values (estimated price, layers with URLs) but lacks details on parameter roles, constraints, or persistence. Not fully complete for effective use.

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 only 20% (only layers has a nested description). The tool description does not compensate for low coverage; it only hints at layers content via 'logo/text' but omits other parameters like colorId, product, preview URLs. Missing parameter meaning beyond 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 clearly states it validates and saves a design (logo/text on T-shirt sides) and returns estimated price and layers with URLs for use in create_order. It specifies a verb and resource, and distinguishes from sibling tools by indicating it prepares design for order creation.

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 usage as a prerequisite for create_order, but does not explicitly state when to use it versus alternatives like get_quote or how it relates to other tools. No exclusion cases or when-not-to-use guidance is provided.

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?

The description adds significant behavioral context beyond annotations: it returns payment instructions, awaits payment, and clarifies the agent's role. No contradiction with annotations.

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 concise (three sentences) and front-loaded with the key action and return value. Every sentence serves a purpose without unnecessary detail.

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 tool with 6 parameters, nested objects, and no output schema, the description covers the core functionality, return values, and prerequisites. It could mention related tools like get_order_status, but it is sufficiently 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?

Schema coverage is 67% and the description only highlights customer.email and consent. It does not add substantial meaning for items or other parameters beyond what the schema provides.

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 creates an order and returns payment instructions, with specific verb 'Založí objednávku' (creates an order). It is distinct from sibling tools like create_design and get_order_status.

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 requires customer.email and consent:true, and notes 'agent neplatí' (agent does not pay), providing clear context for when to use. However, it does not explicitly compare with alternatives.

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 readOnlyHint=true and destructiveHint=false, which the description does not contradict. The description adds value by specifying that the status includes both payment and processing aspects, providing more detail than annotations alone. However, it does not disclose any edge cases or failure behaviors (e.g., if order number is invalid).

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, well-formed sentence that delivers the essential information immediately. Every word contributes to purpose and context. No extraneous content 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?

For a simple read-only tool with one parameter and no output schema, the description is largely adequate: it explains what the tool returns (status components) and the required input. However, given the lack of output schema, the description could be improved by hinting at what the return value looks like (e.g., JSON object with fields). It is still quite complete for its complexity.

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 schema has 0% description coverage for the 'orderNumber' parameter. The description only mentions 'podle čísla objednávky' (according to order number), which merely restates the parameter's purpose. It does not explain expected format (e.g., plain text, numeric), constraints (e.g., length), or examples. The description adds minimal semantic value beyond the 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 clearly states the tool's purpose: it returns the order's payment and processing status based on the order number. The verb 'vrátí' (returns) and specific resource 'stav objednávky' (order status) make the action unambiguous. It distinguishes itself from siblings like 'create_order' and 'get_quote' which involve creation or quotes, not status checking.

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 usage when needing order status, but it does not explicitly state when to use it vs. alternatives like 'get_quote' or 'create_order'. There is no guidance on prerequisites (e.g., the order must exist) or exclusions (e.g., not for modifying orders). The guidance is only implied by the tool's read-only nature.

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 indicate readOnlyHint=true and destructiveHint=false, so the description's claim of calculating price is consistent. However, it adds no additional behavioral context beyond what annotations already provide, missing details on authentication, rate limits, or potential constraints.

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, well-front-loaded sentence with no unnecessary words. Every part 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?

Given 6 parameters, a nested object, and no output schema, the description provides a high-level overview but lacks details on return value format, required parameters, and constraints. It is adequate but has clear gaps.

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 50% schema coverage, the description adds meaning by referencing quantity/sizes, printed sides, and promo code, but it does not explain the 'product' parameter, 'printSideHeightsCm', or the nested 'sizes' object structure. It partially compensates for schema gaps.

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 calculates the price of a configured t-shirt, specifying key inputs like quantity/sizes, printed sides, and promo code. It distinguishes well from siblings such as create_design, create_order, get_order_status, and 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 implies usage when a price quote is needed but does not explicitly state when to use vs alternatives, nor does it mention when not to use or provide context for excluding siblings.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds no additional behavioral context beyond the return content. It does not mention pagination, rate limits, or auth requirements, but the safety profile is already covered by annotations.

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 concise sentence that front-loads the action ('Vrátí'). Every part is necessary and there is no filler. It is optimally sized for an AI agent.

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 no output schema, the description sufficiently explains the return value (catalog with colors, sizes, prices, volume discounts) and the conditional behavior. The tool is simple and the description covers all needed context without gaps.

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% (1 parameter with description). The description echoes the parameter's purpose (id/slug to get one product) but adds the context that it alternates between catalog and single product. This is baseline value given high schema coverage.

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 it returns a catalog of t-shirts/hoodies with colors, sizes, prices, and volume discounts, or a single product by id/slug. It uses a specific verb ('returns') and resource, and distinguishes from sibling tools that involve creating designs/orders or querying statuses/quotes.

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 explains that providing an optional product id/slug returns one product, otherwise the full catalog. It implicitly guides when to use this tool versus alternatives (e.g., get_order_status for orders), but does not explicitly state when not to use it.

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