Get network-effect status for an AI-native product: owner count, unlocked tiers (tier 1 at 10 owners, tier 2 at 50, tier 3 at 100), current dynamic price, and when the next price doubling hits. Requires api_key.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | API key from register_agent | |
| product_id | Yes | Product ID |
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.
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.