MDA DB MCP
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- AlicenseAqualityBmaintenanceGives AI agents structured, typed access to Canadian open data through a single server covering Statistics Canada tables, Census and public use microdata, the Bank of Canada, CMHC, federal agencies and federal, provincial, territorial and municipal open-data portals, in English and French. It offers roughly 200 tools for querying, planning multi-source queries, and generating reproduction code in R, Python, Stata or Julia.367 PyPI1MIT
- AlicenseAqualityDmaintenanceEnables querying China National Bureau of Statistics data including economic indicators, time series, and regional statistics. Supports natural language search, batch queries, comparisons, and analysis across monthly, quarterly, and yearly datasets.3112 npm9Apache 2.0
- AlicenseNot gradedqualityDmaintenanceEnables querying statistical data from China NBS, World Bank, IMF, OECD, BIS, census, and department statistics via MCP tools.Apache 2.0
- AlicenseAqualityCmaintenanceLets Claude search the WRDS catalog, check which datasets and years are available, and run read-only SQL queries or export results to CSV, Parquet, or Excel. Enforces the WRDS AI policy by restricting row-level queries to AI-allowed sources and returning only metadata and counts for other libraries.7MIT
- AlicenseNot gradedqualityCmaintenanceProvides database access capabilities to Claude with support for both SQLite and SQL Server databases. It enables users to manage schemas, execute SQL queries, and export results through natural language interaction.417 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to access official statistical and economic indicator datasets from Eurostat, World Bank, OECD, IMF, WHO, and national statistical offices across 150+ countries through natural-language dataset discovery, inspection, time-series retrieval, SQL queries, CSV export, and account/watchlist management.MIT
TDQS
Scored across 14 tools
Although several tools touch exploration (find_variables, search_metadata, list_tables, describe_table), the descriptions explicitly delineate boundaries—e.g. find_variables searches the semantic harmonization layer while search_metadata does exact substring matching on physical structure. Hierarchical listers (schemas/tables/surveys/sections) and the stats/plot pair are clearly distinct. No two tools appear interchangeable.
Almost all names follow a clean verb_noun snake_case pattern (describe_table, run_query, list_surveys, find_variables, plot_variable, search_metadata, rebuild_metadata_index). Only variable_stats and metadata_index_status deviate into noun_noun form, but they read consistently as 'X_stats'/'X_status' queries and cause no confusion.
14 tools sit comfortably in the well-scoped range for a rich survey-microdata domain. Each tool maps to a distinct workflow stage—discovery, inspection, query, analysis, documentation, index maintenance—so nothing feels padded or redundant.
The surface covers discovery (schemas, tables, surveys, sections, variables), inspection (describe_table, sample_rows), querying (run_query), analysis (variable_stats, plot_variable), authoritative docs (read_guide) and index management, which is strong lifecycle coverage. Minor gaps remain—no result export/download or crosstab/join helpers—but agents can work around these with run_query.