ai-commerce

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

With no annotations, the description carries full behavioral disclosure burden. It provides useful context: returns machine-readable product data, mentions dynamic pricing behavior (prices double at milestones), and states 'No auth required.' This covers key operational characteristics, though it doesn't explicitly say the operation is read-only safe or mention rate limits.

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, front-loaded with the core purpose, then adds key behavioral details. No wasted words or 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 absence of an output schema, the description supplies essential return information (machine-readable product data, pricing, network-effect status). It does not detail pagination or full response structure, but for a browse tool this is adequate, especially with sibling context.

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 does not add parameter-specific meaning beyond the schema; it only enriches the overall tool context (e.g., dynamic pricing) but leaves parameter details to 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 function: 'Browse the AI Commerce product catalog.' It uses a specific verb (browse) and resource (catalog), and the mention of returning product data distinguishes it from sibling tools like get_product_details (which likely fetches a single product) and get_network_status (which focuses on 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 context is clear: this tool is for browsing/searching the catalog, while siblings like purchase_product and get_product_details serve different purposes. However, it does not explicitly state when not to use it or name alternative tools, just implies the catalog browsing use case.

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 transparency burden. It discloses the auth requirement (api_key), the tier thresholds, and that the tool returns dynamic pricing and price-doubling timing. This goes beyond the schema by explaining what the data represents, though it stops short of stating side effects or error behavior.

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-structured sentence that front-loads the action and then lists the key data points. Every clause adds value without any 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?

For a simple read-only tool with no output schema, the description provides enough context: it defines the inputs (implicitly), the required auth, and the expected return content (owner count, tiers, price, doubling). It lacks explicit error/edge-case information but is otherwise complete for the tool's purpose.

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% for both parameters, so the baseline is 3. The description does not add any parameter-specific meaning beyond the schema—'product_id' remains just 'Product ID' and 'api_key' is mentioned again as a requirement but without additional 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 uses a specific verb 'get' and names the exact resource ('network-effect status'), then enumerates the returned fields (owner count, tiers, dynamic price, price doubling). This clearly distinguishes it from siblings like get_product_details and purchase_product.

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 when to use the tool—whenever network-effect status is needed—but does not explicitly state alternatives or conditions for use. There is no comparison to get_product_details or browse_catalog, so the guidance is 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?

No annotations are provided, so the description must fully disclose behavioral traits. It only says 'Get full details' without mentioning read-only status, error handling, response format, or any side effects. The added details about pricing model and AI-native metadata are content hints, not behavioral 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, front-loaded sentence that immediately communicates the tool's purpose and key information. No 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?

For a simple one-parameter lookup tool with no output schema, the description adequately covers the primary context: what it returns (full details, pricing model, AI-native metadata). It does not mention error cases, but given the simplicity and the single parameter, the description is sufficiently complete for 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 100%, so the parameter is already well-documented. The description adds 'by its slug' which reinforces the parameter's role but does not go beyond the schema's explanation. 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?

The description clearly states the tool's function: retrieving full details for a single product by slug. It specifies the resource (product details) and a distinguishing scope ('one product by slug') that differentiates it from sibling tools like browse_catalog, which likely lists multiple 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 the tool is for fetching one specific product's full details, which is clear context for selection. However, it does not explicitly name alternatives or state when not to use it, so it stays at 'clear context, no exclusions' rather than full 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 full disclosure burden. It clearly states that orders are confirmed instantly, returns a download_url, and explains the special delivery mechanism for AI-native products. This gives the agent strong expectations about immediacy and delivery, though it omits error or refund behavior.

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, front-loaded with the core action, and each clause adds useful behavioral or delivery information. No filler or unnecessary 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?

The description covers the main return value, the immediate confirmation trait, and the special delivery path for AI-native products. It lacks explicit error handling or cancellation details, but given the simple schema and sibling context, it is sufficiently complete for an agent to select and invoke the 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?

Schema description coverage is 100%, so the baseline is 3. The description references the API key and product ID within the schema, and adds delivery context, but it does not enhance the semantic understanding of individual parameters beyond what the schema already 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 uses a specific verb ('Purchase') and resource ('product'), making the tool's action unmistakable. It clearly distinguishes from siblings like browse_catalog and register_agent by being the only transactional purchase tool.

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 the correct workflow: use after browsing/getting product details and after registering to obtain an API key. It explicitly notes immediate confirmation (no human approval), which is a key usage consideration, but it does not explicitly name alternatives or state when not to use.

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 full burden of disclosure. It discloses the critical trait that the API key is returned only once and must be stored. However, it does not explain behavior on repeated calls, response format, or other side effects. The one-time key warning is valuable but leaves 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 three tight sentences with no fluff. Each sentence adds necessary info: what the tool does, when to use it, and the critical one-time key warning. The em-dash highlights the storage requirement effectively.

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 tool has 4 parameters, no output schema, and no annotations. The description covers the core action, the timing, and the one-time key warning. It does not describe error cases or repeated-call behavior, but for a simple registration step it is substantially 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 100% and every parameter already has a clear description in the input schema (e.g., 'Agent name', 'Contact email'). The description adds no extra meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 ('Register') and resource ('this AI agent') and clearly states the outcome ('receive an API key'). This distinguishes it from siblings like purchase_product or find_products, which are about browsing or purchasing, not registration.

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?

'Required once before purchasing' explicitly tells the agent when to use this tool and implies it should not be called again. It provides strong timing context, though it does not name alternative tools or describe when not to use it beyond the 'once' constraint.

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