Buy a product with your credits. Products: DePIN supply-telemetry snapshots, DAG manifold API (dag_pro/dag_quant/dag_enterprise), signal feed tiers, GPU compute, MCP unlimited. Use api_key from register_agent.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | Your API key from register_agent | |
| product_key | Yes | Product key to purchase (e.g. dag_pro, mcp_unlimited) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It does disclose that the purchase consumes credits and requires an API key, and it enumerates the products available. However, it does not mention outcomes (e.g., what is returned on success/failure), reversibility, or credit insufficiency handling, leaving some ambiguity.
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 long, highly efficient, and front-loaded with the purpose ('Buy a product with your credits'). The product list is compact and informative, with no redundant phrases.
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 is simple with two parameters and no output schema. The description covers the purpose, products, and a prerequisite (api_key). It lacks details about the response format or post-purchase behavior, but given the simplicity and sibling context, it is largely sufficient.
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 schema covers both parameters with descriptions, and the tool description adds specificity by naming the API key source (register_agent) and providing product examples (dag_pro, mcp_unlimited). This goes beyond the schema's generic 'Product key to purchase' description, so the added value warrants above baseline.
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 a specific verb ('Buy'), the resource ('product'), and the payment method ('credits'). It also enumerates the specific product catalog, which distinguishes it from sibling tools like topup_credits (adds credits) and register_agent (creates API key).
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 provides clear context: use this tool to spend credits on listed products, and it references the prerequisite API key from register_agent. It doesn't explicitly contrast with alternatives, but the context and sibling list make the intended usage unambiguous. It could be improved by stating when not to use it (e.g., before topup_credits).
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 target distinct actions (browse, query, purchase, top-up), but get_zk_provenance and verify_footprint both promise cryptographic verification for datasets/records and could easily be confused. fetch_dataset vs get_stats also has mild overlap, but the descriptions clarify querying records vs. aggregate statistics.
All tools follow a consistent snake_case verb_noun pattern: check_balance, fetch_dataset, list_products, purchase_data, register_agent, etc. The verbs are action-oriented and the nouns correspond to the resource being handled.
Ten tools cover registration, billing, product browsing, data querying, stats, and verification without feeling bloated. The count is typical for a marketplace/API server and each tool serves a concrete step in the user journey.
The core lifecycle is complete: register, view credits, browse products, buy, query, verify, and top up. Minor gaps exist around post-purchase management, such as listing owned subscriptions/entitlements or managing/rotating the API key, but agents can complete the main workflows without dead ends.