agentsvc-mcp

macro_data

Fetch country-level macroeconomic indicators such as inflation, GDP, unemployment, and debt from World Bank yearly data; optionally include ECB policy rates for economic analysis.

Instructions

Macroeconomic Data — Macroeconomic indicators per country from World Bank Open Data with yearly history: inflation, GDP, GDP growth, GDP per capita, unemployment, population, government debt, current account, exports, lending rate. Optionally current ECB policy rates. Params: country (ISO2 or ISO3, required), indicators (default inflation, gdp, gdp_growth, unemployment, population), years (1 to 20, default 5), include_ecb_rates (default false). Cost: $0.006 USDC per call. Latency p99: 5000ms.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "country": "<country>"
    },
    "name": "macro_data"
  }
}

Input Schema

Name	Required	Description	Default
`years`	No
`country`	Yes
`indicators`	No
`include_ecb_rates`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 21, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral burden and does well: it discloses the data source, yearly granularity, the paid cost ($0.006 USDC per call), and p99 latency (5000ms). It stops short of auth requirements, rate limits beyond pricing, or error behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense paragraph that front-loads the resource and data source, then the params, then cost and latency. The indicator enumeration is long but each item is load-bearing; there is little filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a four-parameter tool with no output schema and no annotations, the description covers source, scope, parameter semantics, cost, and latency. What it lacks is any note on the shape of the returned yearly series, which the missing output schema leaves unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must do all the work — and it does. It documents every parameter: country format (ISO2/ISO3, required), the indicator list and default set, years range (1-20, default 5), and include_ecb_rates (default false).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific domain (macroeconomic indicators per country) and the exact data source (World Bank Open Data) and enumerates the indicators returned. This clearly separates it from adjacent siblings such as exchange_rates, crypto_prices, or company_lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the scope statement (country-level macro indicators), but there is no explicit when-to-use/when-not guidance and no alternative tool is named. An agent can infer the fit, but nothing routes it explicitly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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