World Bank country record by ISO code: region, income group, lending category, capital city and coordinates. $0.005 per call via x402 (USDC on Base); response includes a provenance chain and an Ed25519 attestation.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| payment | No | Encoded x402 X-PAYMENT header value. Omit to receive the payment requirements (free quote). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the cost ($0.005 per call via x402), the payment mechanism (USDC on Base), and the response characteristics (provenance chain and Ed25519 attestation). This goes beyond the schema and gives the agent critical operational knowledge, though it does not explicitly state that the operation is read-only or describe rate limits.
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. The first sentence defines the core purpose and output, the second adds the critical payment and response details. No fluff; every clause adds value. The most important information is front-loaded, making it easy to scan.
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 lookup tool with two parameters and no output schema, the description covers the essentials: what it returns, the cost, and the payment flow. It does not detail the exact JSON structure of the response, but the listed fields and attestation hint are sufficient for most agents. The payment requirement is explicitly explained, preventing failed calls.
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 only documents the 'payment' parameter (50% coverage), leaving 'id' undocumented. The description compensates by specifying that 'id' is an ISO code, adding semantic meaning. For 'payment', the schema already explains its purpose, so the description doesn't need more. Together, the description and schema provide adequate meaning for both parameters.
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 retrieves a World Bank country record by ISO code, listing the specific fields returned (region, income group, lending category, capital city, coordinates). This distinguishes it from sibling lookup tools like 'uk_company_lookup' or 'usgs_quake_lookup' by naming the exact resource and data types.
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 usage context by explaining the payment model and how to obtain a free quote (omit payment field). It does not explicitly list alternatives, but since no sibling tool covers World Bank country data, the unique purpose makes the usage obvious. No exclusion criteria are given, but they are unnecessary.
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 unique dataset and operation: lookups by ID, summary aggregations, time series, or search. Even tools with similar descriptors (e.g., FMRArea lookup vs. search, FDA vs. CPSC recalls) are clearly separated by resource and output type.
All tool names are lowercase snake_case and mostly follow a `domain_resource_kind` pattern such as `fda_recall_lookup` and `cpsc_recall_monthly_summary`. A few outliers like `bank_profile_lite`, `cpi_us_monthly`, and `us_debt_to_penny` break the dominant suffix convention but remain readable.
24 tools is on the high side for a single server, but this appears to be an aggregator of many independent public datasets, so each tool represents a distinct data source and has a purpose. It is slightly above the ideal ergonomic range but not bloated or redundant.
As a read-only attested-data lookup service, the set provides good coverage with both point lookups and aggregate summaries across many domains. The main gaps are the lack of a catalog/discovery endpoint and search support for most identifier-based lookups, but agents can work around those with known identifiers.