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World Bank Data + Analysis

wb_series
Read-only

" for , over time / vs other countries" — World Bank annual data, feature-extracted (open data, 3 credits). Pass a structural CONCEPT (the server resolves it to the canonical indicator + variant) or an exact indicator id. Returns results:[{country, series:[{year,value}], analysis}] (one per country); analysis = latest(+year), trend, cagr_pct, change, peak/trough. ⭐ For a MULTI-COUNTRY call (comma-sep country) a top-level ranking orders them by the latest value — the cross-country compare FRED can't do. Annual STRUCTURAL data → read levels + CAGR, not high-frequency moves. A country/year with no data is omitted (never interpolated). The right VARIANT (%GDP vs US$ vs per-capita) is picked from your context. A concept that isn't World-Bank structural (CPI/unemployment → fred_series) → honest no_match. Surface _license_note when showing data.

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": {
      "q": "<q>",
      "country": "<country>"
    },
    "name": "wb_series"
  }
}

Input Schema

NameRequiredDescriptionDefault
qYesA cross-country STRUCTURAL concept (any language — 'household consumption', 'GDP per capita', 'poverty rate', 'life expectancy', 'government debt', 'การบริโภค') OR an exact World Bank indicator id you already hold (WB_WDI_NE_CON_PRVT_ZS). The SERVER resolves a concept → the canonical indicator + the right unit VARIANT (no separate lookup). 🔴 Pass the user's FULL request in `context` so the server picks the VARIANT: % of GDP/share (compare across country sizes) vs current US$ (absolute) vs per-capita (living standards) vs constant/real. World Bank = ANNUAL structural data (consumption/GDP/demographics/poverty) — NOT high-frequency macro (CPI/unemployment/rates → fred_series).
toNoEnd year (default latest).
from_NoStart year (default ≈ last 15y, dynamic).
contextNoOPTIONAL but RECOMMENDED — the user's FULL request VERBATIM (any language). Used to pick the right unit VARIANT (% of GDP vs US$ vs per-capita vs real). e.g. 'compare household spending as a share of GDP'.
countryYesCountry as ISO3 or a name (`Thailand`/`THA`); COMMA-SEP for a cross-country compare (`THA,JPN,USA`); or an aggregate (`WLD` world · `EMU` euro area · `HIC`/`MIC`/`LIC` income groups).
databaseNoADVANCED — the indicator's source database (WB_WDI, IMF_FSIC). Auto-derived from the resolved concept; override only for a non-WDI id you pass directly.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations only declare readOnlyHint, but the description adds real behavioral context: a 3-credit cost, no interpolation (missing country/years are omitted), unit variant auto-selected from `context`, an honest no_match for non-World-Bank concepts, and a `_license_note` that must be surfaced. Not disclosed: error shapes, timeout/rate behavior, or whether repeated calls are cached.

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

Conciseness3/5

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

All the content is relevant, but it is delivered as one dense block with symbols (⭐, 🔴), parenthetical asides, and repeated rules (concept-vs-id and the FRED exclusion are stated in both the prose and the schema). Front-loading a query template instead of a purpose statement makes the first read slower than necessary.

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 6-parameter read tool with no output schema, the description carries the return contract well: `results[{country, series[{year,value}], analysis}]`, the `analysis` fields (latest/trend/cagr_pct/change/peak/trough), the `ranking` for multi-country calls, and `_license_note`. Only failure/pagination behavior is left implicit.

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

Parameters3/5

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

Schema coverage is 100% and the parameter descriptions already cover the concept/id dual mode, comma-separated countries, aggregates, and the advanced `database` override, so the description largely restates structured data. It does add one consequence the schema does not: a multi-country `country` produces a top-level `ranking`. Baseline 3 applies given the schema does the heavy lifting.

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

Purpose4/5

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

States the resource and scope precisely: World Bank annual structural indicator data for a country, resolved from a concept or an exact indicator id, returned with trend/CAGR analysis. It explicitly distinguishes itself from fred_series for high-frequency macro (CPI/unemployment). The opening is a query template ("<structural indicator> for <country>") rather than a plain statement of purpose, and "feature-extracted" is never explained, so it falls just short of a 5.

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

Usage Guidelines5/5

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

Gives explicit when-to-use (annual structural levels: consumption/GDP/demographics/poverty), when-not (daily/high-frequency macro → fred_series), and the alternative for the concept-vs-id decision (server resolves concepts; pass an id only if you already hold one). It also names the multi-country compare path via comma-separated country codes and the aggregate-country option.

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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