okun-koneosa

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

Beyond the annotations, the description adds key behaviors: the link sets quantities rather than adds, is idempotent (clicking twice gives same result), stock is re-checked with potential quantity reduction or row skipping, and checkout requires customer account. This goes well beyond the annotation flags and provides rich operational detail.

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 lengthy but well-structured into Args, Returns, Errors, and Notes sections. Each sentence adds value, though there is slight redundancy with annotations (e.g., 'writes nothing'). It could be tighter, but the organization aids readability.

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 tool's complexity, the description covers purpose, parameters, return format, error handling, and nuanced behaviors (idempotency, stock re-check, checkout auth). With an output schema and annotations present, the description provides comprehensive guidance for an agent to select and invoke the tool correctly.

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?

Even though schema coverage is 100%, the description adds crucial semantics: repeated product_ids are merged with quantities summed, unknown fields are rejected, explicit ranges for items and quantity, and the effect of the checkout flag. This adds meaning beyond the raw schema and helps the agent construct valid 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?

The description states 'Build a link that puts the given products into the customer's cart' – a specific verb+resource that clearly distinguishes it from sibling query tools. It also clarifies the tool writes nothing and returns a URL, leaving no ambiguity about its function.

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?

Provides clear context: 'Give the URL to the customer and let them click it.' Implicitly distinguishes from sibling tools (which are for querying) but doesn't explicitly state when not to use or name alternatives. The use case is evident, though not 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: stock is fetched fresh from a separate endpoint, it returns an error for unknown/unpublished ids, and out-of-stock products become unpublished. No contradiction.

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 well-structured with clear sections: Args, Returns, Notes. It is concise (every sentence adds value) and front-loaded with the primary purpose. No unnecessary 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?

For a simple retrieval tool with 1 param, annotations, and output schema, the description is complete. It covers behavior, error conditions, sourcing of parameters, and relationship to other tools.

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 has 1 param with description 'Product ID from search_catalog'. The description clarifies it is numeric and from search_catalog, adding meaning beyond the schema. Since schema coverage is 100%, baseline is 3, but the description provides useful sourcing context.

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 'Get full details and current stock for one Okun Koneosa product.' It uses a specific verb ('get') and resource ('product details'), and distinguishes itself from sibling tools like 'search_catalog' (searching) and 'create_cart_link' (building a cart link).

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 when to use this tool (to get more current stock than search_catalog) and provides error handling notes. It also mentions that product_id comes from search_catalog, 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.

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds behavioral details: stock status, pricing including VAT, free shipping conditions, and notes that image_url is often null. It also mentions that prices are confirmed at checkout.

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 well-structured with clear sections (Args, Returns, Notes) and front-loaded with the purpose. While slightly verbose, it is organized and each 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?

Given the 6 parameters and existing output schema, the description is fully comprehensive. It covers the catalog context, parameter details, return structure, and important behavioral notes, leaving no gaps for the agent.

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 provides detailed explanations for all 6 parameters, including specific options for sort, valid ranges for limit and page, and the behavior of in_stock_only. This adds significant meaning beyond the input schema, which has 67% 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 'Search the Okun Koneosa product catalog' and provides extensive context about the store and its inventory. It distinguishes itself from sibling tools by explicitly referencing get_product_details for full descriptions.

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 when to use the tool (e.g., free-text search, browsing by category) and mentions alternatives like get_product_details for full descriptions. However, it does not explicitly say 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.

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

Adds substantial behavioral detail beyond annotations: in_stock_only rarely changes results, image_url is usually null, prices are confirmed at checkout, and name_raw preserves supplier spelling. No contradiction with readOnlyHint.

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?

Well-organized with Args/Returns/Notes sections; every sentence adds useful information without 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?

Covers business context (store, stock, VAT, shipping), parameter nuances, output structure, and handles edge cases like missing images. Even provides a pointer to a related tool for more detail.

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?

Description enriches all parameters significantly, e.g., query matching name/SKU/EAN and empty-to-browse, category_id pointing to an external category list, and clear semantics for in_stock_only.

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 searches the Okun Koneosa product catalog, a specific resource. However, it does not differentiate from the sibling tool 'search_catalog', which may overlap in function.

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?

Provides clear context (shop catalog search, browse by category) and explicitly recommends get_product_details for full descriptions and fresh stock checks. Does not address when to prefer search_catalog or other 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?

Discloses that content is split by heading to return relevant paragraphs, advises to cite url, and notes that shop terms do not override Finnish law. Annotations are consistent and description adds value beyond them.

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?

Well-structured with Args, Returns, and Notes sections. Front-loaded with purpose. Every sentence adds necessary information without 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 2 simple parameters, an output schema described in Returns, and annotations, the description provides all needed context including behavior, limitations, and guidance. No 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?

Input schema covers both parameters with descriptions, and the description adds concrete examples (e.g., 'palautus', 'toimitusaika') and explains behavior when query is empty. Schema coverage is 100% but description enriches usage.

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?

Clearly states the tool searches Okun Koneosa's shop policies and FAQs. Lists specific topics (delivery, privacy, returns, etc.) and distinguishes from sibling tools like search_catalog which handles 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?

Explicitly says 'Use this instead of guessing when the customer asks about delivery times, shipping costs, returns, warranty or how their data is handled.' Provides concrete usage context and alternatives.

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