World Bank MCP Server
[](https://mseep.ai/app/anshumax-world-bank-mcp-server)
# World Bank MCP Server
[](https://lightnow.ai/servers/io.github.anshumax/world_bank_mcp_server)
A Model Context Protocol (MCP) server that enables interaction with the open World Bank data API. This server allows AI assistants to list indicators and analyse those indicators for the countries that are available with the World Bank.
## Features
- List available countries in the World Bank open data API
- List available indicators in the World Bank open data API
- Analyse indicators, such as population segments, poverty numbers etc, for countries
- Comprehensive logging
## Usage
### With Claude Desktop
Add this to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"world_bank": {
"command": "uv",
"args": [
"--directory",
"path/to/world_bank_mcp_server",
"run",
"world_bank_mcp_server"
]
}
}
}
```
### Installing via Smithery
To install World Bank Data Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@anshumax/world_bank_mcp_server):
```bash
npx -y @smithery/cli install @anshumax/world_bank_mcp_server --client claude
```
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'get_indicator_for_country' follows a clear verb_noun pattern.
A single tool is too few for a server named 'World Bank MCP Server', which implies access to a broad dataset (e.g., indicators, countries, years). This minimal surface will likely cause agent failures due to lack of flexibility (e.g., no search, filtering, or multi-country queries).
The tool set is severely incomplete for the domain. It only allows fetching a single indicator for a single country, missing essential operations like listing indicators, getting data for multiple countries or years, searching metadata, or accessing other World Bank datasets (e.g., projects, finances).