Browse the AI Commerce product catalog. Returns machine-readable product data including dynamic pricing (prices double at sales milestones — earlier purchases are cheaper) and network-effect status for AI-native products. No auth required.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results | |
| search | No | Free-text search over name/description | |
| category | No | Filter by category (prompt, guide, workflow, agent) | |
| max_price | No | Maximum price in USD |
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.
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.