Purchase a product with your API key. Order is confirmed instantly (no human approval). Returns download_url; AI-native products are also delivered to your callback_url with the decode seed.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | API key from register_agent | |
| product_id | Yes | Product ID from browse_catalog | |
| coupon_code | No | Optional coupon code |
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.
Add one secure layer between your agents and this server.
Most tools are clearly distinct: browsing, specific product details, network status, genesis artifacts, purchase, and registration are separate actions. There is slight overlap between browse_catalog and get_product_details (one lists all, one gets one), but their scope is different enough to avoid misselection.
All tool names follow a consistent verb_noun pattern in snake_case: browse_catalog, get_genesis_status, get_network_status, get_product_details, purchase_product, register_agent. This is uniform and predictable.
With 6 tools, the server is well-scoped. It covers the essential operations for an AI-commerce platform without being bloated or sparse. Each tool serves a necessary purpose in the customer lifecycle.
The tool surface covers the core flow: registration, browsing, product details, network status, and purchasing. It lacks order history or a way to list owned products, but the purchase returns a download URL, so agents can still complete transactions. Minor gaps that do not break the primary workflow.