Browse all purchasable products with prices in credits ($1 = 1 credit). Includes DePIN supply-telemetry data, signal feed tiers ($9.99-$399.90/mo), DAG manifold API ($2.99-$9.99/mo), GPU compute, and MCP unlimited access. Call after register_agent to see what to buy.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It indicates a read-only action via 'Browse' and includes pricing/credit context, but it does not disclose output format, pagination, authorization requirements, or any behavior if called before register_agent. For a tool with no annotations and no output schema, this is insufficient.
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 three concise sentences. The first sentence front-loads the purpose, the second gives concrete examples with prices, and the third offers actionable guidance. Every sentence earns its place and there is no 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?
The description covers the essential product scope and pricing, and gives usage context. Yet, without an output schema, it does not describe the response structure, field names, or pagination, which would be helpful for a list tool. Still, the provided content is largely sufficient for an agent to understand what to expect.
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 tool has zero parameters and the schema is empty, so the baseline according to the rubric is 4. The description adds context about product categories and pricing, but there is no parameter-specific information to clarify. This is appropriate for a 0-parameter tool.
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 purpose: 'Browse all purchasable products with prices in credits.' It lists specific product categories and price ranges, and explicitly distinguishes itself from sibling tools like register_agent by saying 'Call after register_agent to see what to buy.' This is a specific verb (browse) + resource (purchasable products) with clear differentiation.
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 explicit usage guidance: 'Call after register_agent to see what to buy,' which tells the agent when to use this tool. However, it does not mention alternatives or when not to use it, so it stops short of a full 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 (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.