list_opportunity_datasetsFree catalog lookup. Lists the raw Japanese public-sector business-opportunity datasets on this gateway (open tender notices, subsidy programs, procurement sanction lists) with per-call x402 pricing (0.05 USDC per call). Use the search/get tools or the returned api_url to retrieve data.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses pricing (0.05 USDC per call) and clarifies it is a free catalog lookup (read-only, no data retrieval). No annotations provided, so description carries full burden and does so effectively.
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?
Three sentences, each essential: states it's a free catalog, lists dataset types with pricing, then provides next-step guidance. No wasted words.
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?
Covers purpose, pricing, and follow-up actions. Lacks explicit description of the output structure (e.g., array of objects with fields), but given the simplicity and mention of api_url, it is mostly 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?
No parameters exist, so baseline is 4. Description adds value by explaining the tool's purpose, but no parameter details are needed.
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?
Clearly states the tool lists Japanese public-sector business-opportunity datasets, specifying types (open tender notices, subsidy programs, procurement sanction lists). Differentiates from siblings by focusing on the catalog listing vs. specific get/search operations.
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?
Explicitly advises using search/get tools or the returned api_url for data retrieval, and mentions pricing. Could be more direct about when not to use this tool, but context is clear.
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.
Each tool targets a distinct operation: catalog listing, free search for subsidies/tenders/sanctions, and paid detail retrieval for subsidies/tenders. No ambiguity between tools.
Tool names follow a consistent verb_noun pattern: 'list_' for catalog, 'search_' for free queries, and 'get_' for paid detail retrievals. The pattern is predictable and clear.
Six tools cover the core needs: listing available datasets, searching three categories (subsidies, tenders, sanctions), and retrieving full records for two of them. This is well-scoped for a specialized gateway.
The set covers search and detail retrieval for subsidies and tenders, plus sanctions search. Missing a 'get_sanction' tool for full sanctions records, but the search tool returns sufficient data for most use cases. The paid detail tools currently only initiate payment, but that is by design in private beta.