World Bank Data360 MCP Server
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FlicenseNot gradedqualityCmaintenanceProvides LLM agents direct access to World Bank development indicators, enabling search, validation, and retrieval of data on topics like GDP, poverty, and gender equality.40-- AlicenseNot gradedqualityBmaintenanceEnables AI agents to access and query World Bank data (free, no auth) through natural language or direct tools, wrapping the World Bank Data API v2.171 npmMIT
- FlicenseBqualityDmaintenanceEnables AI assistants to interact with the World Bank open data API, allowing for listing and analysis of indicators across available countries.150-
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- AlicenseAqualityBmaintenanceEnables AI assistants to discover, retrieve, and compare official international development indicators from sources such as the World Bank, FAOSTAT, WHO, UNICEF, and IMF, while preserving source identifiers, units, and citations.102MIT
- AlicenseNot gradedqualityAmaintenanceEnables querying 29,500+ World Bank development indicators for 200+ countries across 60+ years via MCP, with 7 tools for browsing topics, sources, countries, and indicators.148 npm3Apache 2.0
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose with no overlap: search_datasets_tool and search_local_indicators serve different search functions (API vs. offline), get_temporal_coverage_tool handles year availability, retrieve_data_tool fetches actual data, and list_popular_indicators provides curated discovery. The descriptions explicitly differentiate their roles, preventing agent misselection.
The naming is mixed, with some tools using verb_noun (search_datasets_tool, retrieve_data_tool) and others using noun_verb (get_temporal_coverage_tool, list_popular_indicators). While all names are readable and descriptive, the inconsistency in verb placement and suffix usage ('_tool' on some but not others) reduces predictability. The pattern is not chaotic but lacks uniformity.
With 5 tools, the count is well-scoped for a World Bank data server, covering essential workflows: discovery (search_local_indicators, list_popular_indicators), search (search_datasets_tool), validation (get_temporal_coverage_tool), and retrieval (retrieve_data_tool). Each tool earns its place without bloat, supporting a clear data access pipeline.
The tool set covers the core data retrieval workflow comprehensively: search, temporal validation, and data fetching, with additional discovery aids. Minor gaps exist, such as no explicit tools for filtering or aggregating data beyond basic parameters, but agents can work around these using the provided tools. The surface supports end-to-end data access without dead ends.