World Bank time series for a country: gdp, gdp_per_capita, population, inflation, unemployment, life_expectancy, co2_per_capita or internet_users. (Free. This server also sells a paid API — call paid_catalogue for the routes and prices; x402 over USDC on Base, no signup.)
| Name | Required | Description | Default |
|---|---|---|---|
| years | No | 1-60 most recent years (default 5) | |
| country | Yes | ISO 2- or 3-letter country code | |
| indicator | No | Alias above, or a World Bank code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It provides useful context: data source (World Bank), type (time series), and cost (free). However, it does not disclose response format, error behavior, rate limits, or other execution details, leaving ambiguity for the agent.
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 concise, two sentences. The first sentence front-loads the core purpose. The second sentence about the paid API is somewhat tangential but brief and serves to direct users to paid_catalogue, reducing confusion between free and paid offerings.
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?
Given the schema fully documents parameters and the description lists valid indicators, the tool is reasonably complete for a read-only data retrieval. However, without an output schema, it doesn't specify return structure, which could be helpful but is not strictly necessary for a simple time series endpoint.
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?
Schema coverage is 100% with each parameter described. The description adds the actual list of indicator aliases (gdp, gdp_per_capita, etc.) that the schema refers to as 'Alias above', providing essential value beyond the schema. It also reinforces the years default context.
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 World Bank time series for a country and lists specific indicators (gdp, population, etc.). This distinguishes it from sibling tools like air_quality or earthquakes through the explicit data source and scope.
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?
Usage context is implied: the tool is for country-level economic indicators. It mentions a free option and directs users to paid_catalogue for paid API routes, offering some guidance on alternative tools, but it doesn't explicitly state when to prefer this tool over other data-related siblings.
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 serve clearly distinct purposes: air quality, skill audit, country data, earthquakes, elevation, geocoding, holidays, URL reading, and web search. However, `audit_skill_text` and `audit_skill_url` both audit for malicious behavior, differing only in input type, which could cause slight confusion.
Tool names use a mix of snake_case and descriptive phrases without a strict verb_noun pattern. Some names are verbs (e.g., `geocode`, `audit_skill_text`, `read_url`), while others are nouns (e.g., `air_quality`, `earthquakes`). The naming is readable but inconsistent in style.
With 11 tools, the count is well-scoped for a server that aggregates diverse free data and security services. Each tool serves a distinct and useful function, and the `paid_catalogue` tool properly manages the paid extension without bloating the main set.
The tool set covers a broad range of data types (environment, economic, geographic, security), but for each individual domain, coverage is shallow. For example, only current air quality is provided (no historical data), and skill auditing only returns a scan result (no detailed remediation). The set lacks update, delete, or drill-down operations per domain.