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?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only implies credit deduction with 'with your credits' and mentions the api_key prerequisite. It fails to state irreversibility, what happens on insufficient credits, failure modes, or the response format. This is a significant transparency gap for a mutation/purchase tool.
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?
Two focused, front-loaded sentences. The first states the action, the second provides useful product enumeration and auth guidance. No filler or repetition.
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 2-parameter tool with no output schema or annotations, the description covers the product list and authentication prerequisite adequately. However, it omits what the tool returns (e.g., order ID, success message) and does not mention error scenarios, leaving a minor completeness gap.
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 already describes both parameters with 100% coverage, including examples for product_key (dag_pro, mcp_unlimited). The description's list of product categories adds mild context but does not fundamentally enhance parameter understanding beyond 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 a specific verb and resource: "Buy a product with your credits." It distinguishes this tool from siblings by being the actual purchase action, and it lists example products, making its scope unmistakable.
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 gives clear context: you need credits and an api_key from register_agent. It does not explicitly say 'use list_products to browse items' or 'use topup_credits if short on credits,' but the context is sufficient to infer when to use this tool. No exclusions are mentioned, preventing a 5.
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 (register, list, purchase, fetch, verify). The only ambiguity is between get_zk_provenance and verify_footprint, both providing cryptographic proofs, but they serve slightly different purposes (record provenance vs dataset footprint).
All tools follow a consistent snake_case verb_noun pattern (e.g., list_datasets, purchase_data, check_balance), making the set predictable.
10 tools is appropriate for the server's scope, covering user management, data browsing, purchasing, and verification without redundancy.
The set supports the full workflow: register, browse products/datasets, purchase, fetch data, check/top-up credits, and verify provenance. Minor gap: no way to view purchase history or account details, but core functionality is complete.