OECD MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@OECD MCP Server Compare GDP growth between US and Germany"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
OECD MCP Server
A Model Context Protocol (MCP) server providing AI assistants access to OECD's 5,000+ economic and statistical datasets via SDMX API. By connecting to the MCP server, you can use AI to search, analyze, and compare data from 38 OECD countries across economy, health, education, environment, and more.
En Model Context Protocol (MCP) server som ger AI-assistenter tillgång till OECD:s 5,000+ ekonomiska och statistiska dataset via SDMX API. Genom att ansluta till MCP-servern kan du med hjälp av AI söka, analysera och jämföra data från 38 OECD-länder inom ekonomi, hälsa, utbildning, miljö och mer.
Quick Start
Det är enkelt att ansluta en LLM / AI-chatbot till MCP-servern. Det enklaste sättet är att använda npm-paketet oecd-mcp via npx. Nedan finner du guider för olika klienter.
The easiest way to connect an LLM / AI chatbot to the MCP server is via the published npm package oecd-mcp using npx. Below you'll find guides for different clients.
Claude Desktop
1. Open Claude Desktop settings → Developer → "Edit Config"
2. Add to the JSON file:
{
"mcpServers": {
"oecd": {
"command": "npx",
"args": ["-y", "oecd-mcp"]
}
}
}3. Save and restart Claude Desktop
Claude Code
claude mcp add oecd npx -y oecd-mcpVerify: claude mcp list
OpenAI Codex
~/.codex/config.toml:
[mcp.oecd]
command = "npx"
args = ["-y", "oecd-mcp"]
transport = "stdio"From Source
git clone https://github.com/isakskogstad/OECD-MCP.git
cd OECD-MCP
npm install
npm run buildThen point your client at node /absolute/path/to/OECD-MCP/dist/index.js, e.g.:
{
"mcpServers": {
"oecd": {
"command": "node",
"args": ["/absolute/path/to/OECD-MCP/dist/index.js"]
}
}
}Related MCP server: WHO MCP Server
Features
Servern ansluter till OECD SDMX API och ger tillgång till 5,000+ dataset i 17 kategorier.
OECD SDMX API Tillgång till kvartalsvis nationalräkenskaper (QNA), huvudsakliga ekonomiska indikatorer (MEI), hälsostatistik, utbildningsdata, miljöindikatorer och mer från 38 OECD-medlemsländer plus partnerekonomier.
MCP-servern implementerar MCP-protokollet med stöd för:
9 verktyg — Dataset-upptäckt (5) och dataåtkomst (4)
7 resurser — Kategorier, länder, filterguide, ordlista, LLM-instruktioner
7 promptmallar — Ekonomisk analys, landsjämförelser, nordiskt fokus
The server connects to the OECD SDMX API providing access to 5,000+ datasets across 17 categories:
OECD SDMX API Access to quarterly national accounts (QNA), main economic indicators (MEI), health statistics, education data, environmental indicators, and more from 38 OECD member countries plus partner economies.
The MCP server implements the MCP protocol with support for:
9 tools — Dataset discovery (5) and data access (4)
7 resources — Categories, countries, filter guide, glossary, LLM instructions
7 prompt templates — Economic analysis, country comparisons, Nordic focus
Tools
Tool | Description |
| Search for datasets by keyword |
| List datasets by category |
| List all 17 data categories |
| Get commonly used datasets |
| Search for specific indicators |
| Get metadata for a dataset |
| Query statistical data |
| Generate OECD Data Explorer link |
| Detailed category information |
Resources
Resource | Description |
| 17 data categories with descriptions |
| Curated popular datasets |
| ISO 3166-1 alpha-3 country codes |
| SDMX filter syntax guide |
| Definitions of OECD terms |
| Instructions for AI assistants |
| API information |
Prompt Templates
Prompt | Description |
| Analyze economic trends over time |
| Compare data between countries |
| Get latest statistics |
| Guided dataset exploration |
| Find the right dataset for a question |
| Help building SDMX filters |
| Compare Nordic countries |
Use Cases
Ekonomer & Forskare
Verktyg:
Verktyg | Beskrivning |
| Fråga BNP, inflation, arbetslöshetsdata |
| Hitta dataset för forskningsfrågor |
| Förstå dataset-dimensioner |
Exempel:
"Jämför BNP-tillväxt mellan G7-länderna 2020-2024"
"Analysera inflationstrender i eurozonen"
"Hitta data om arbetsproduktivitet per sektor"
App-exempel: Mobil ekonomisk brief med BNP-trender och jämförelsediagram.
Policyanalytiker
Verktyg:
Verktyg | Beskrivning |
| Bläddra i 17 policyområden |
| Hitta relevanta policy-dataset |
| Generera länkar för rapporter |
Exempel:
"Vilken sjukvårdsutgiftsdata finns för Sverige?"
"Hitta utbildningsresultatindikatorer för PISA-analys"
"Jämför miljöpolicyer i nordiska länder"
App-exempel: Hälsostatistik-dashboard med donutdiagram och landjämförelser.
Datajournalister
Verktyg:
Verktyg | Beskrivning |
| Hitta specifika mätvärden |
| Få tillgång till ofta citerad data |
| Hämta siffror för artiklar |
Exempel:
"Hämta senaste arbetslöshetssiffror för EU-länder"
"Hitta inkomstojämlikhetsdata (Gini-koefficient)"
"Jämför skatteintäkter som % av BNP"
App-exempel: Produktivitetsjämförelse med stapeldiagram och trendlinjer.
Economists & Researchers
Tools:
Tool | Description |
| Query GDP, inflation, unemployment data |
| Find datasets for research questions |
| Understand dataset dimensions |
Examples:
"Compare GDP growth between G7 countries 2020-2024"
"Analyze inflation trends in the Eurozone"
"Find data on labor productivity by sector"
App Example:
Mobile economic brief with GDP trends, G7 comparison charts, and sparkline visualizations from the QNA dataset.
Policy Analysts
Tools:
Tool | Description |
| Browse 17 policy areas |
| Find relevant policy datasets |
| Generate links for reports |
Examples:
"What healthcare spending data is available for Sweden?"
"Find education outcome indicators for PISA analysis"
"Compare environmental policies across Nordic countries"
App Example:
Health statistics dashboard with donut chart overview and country comparison from the HEALTH_STAT dataset.
Data Journalists
Tools:
Tool | Description |
| Find specific metrics |
| Access commonly cited data |
| Get numbers for stories |
Examples:
"Get latest unemployment figures for EU countries"
"Find income inequality data (Gini coefficient)"
"Compare tax revenues as % of GDP"
Data Categories
Category | Code | Description | Datasets |
Economy | ECO | GDP, growth, inflation, interest rates | 500+ |
Employment | JOB | Labor market, wages | 300+ |
Trade | TRD | International trade | 250+ |
Health | HEA | Healthcare, life expectancy | 200+ |
Education | EDU | PISA, outcomes | 180+ |
Environment | ENV | Climate, emissions | 150+ |
Innovation | STI | R&D, patents, AI | 140+ |
Energy | NRG | Production, renewables | 120+ |
Taxation | TAX | Tax revenues, tax rates | 100+ |
Finance | FIN | Markets, banking | 95+ |
Government | GOV | Governance, public sector | 90+ |
Social | SOC | Inequality, quality of life | 85+ |
Agriculture | AGR | Production, food security | 75+ |
Industry | IND | Industrial production | 70+ |
Development | DEV | Development aid | 60+ |
Transport | TRA | Infrastructure, mobility | 50+ |
Regional | REG | Subnational data | 45+ |
About
Skapad av: Isak Skogstad
Kontakt: isak.skogstad@me.com
Fristående: Detta projekt är fristående och är inte anslutet till, godkänt av eller officiellt kopplat till OECD (Organisationen för ekonomiskt samarbete och utveckling).
Användarvillkor: Data tillhandahålls via OECD:s öppna SDMX API. Se OECD:s användarvillkor för användningsvillkor.
Created by: Isak Skogstad
Contact: isak.skogstad@me.com
Disclaimer: This project is independent and not affiliated with, endorsed by, or officially connected to the OECD (Organisation for Economic Co-operation and Development).
Terms of use: Data is provided via OECD's open SDMX API. See OECD Terms and Conditions for usage conditions.
Resources
OECD Data Portal: https://data.oecd.org/
SDMX Standard: https://sdmx.org/
MCP Documentation: https://modelcontextprotocol.io/
npm Package: https://www.npmjs.com/package/oecd-mcp
Issues: GitHub Issues
Built with Model Context Protocol SDK | Version 4.0.0
Available Tools
9 toolsget_categoriesA
Get all available OECD data categories (17 categories covering all topics: Economy, Health, Education, Environment, etc.)
Inputs: no named parameters.
get_dataflow_urlA
Generate an OECD Data Explorer URL for a dataset. Use this to provide users with a direct link to explore data visually in their browser.
Inputs: dataflow_id (required), filter.
get_data_structureA
Get the metadata and structure of a specific OECD dataset. Returns dimensions, attributes, and valid values for querying data.
Inputs: dataflow_id (required).
get_popular_datasetsA
Get a curated list of commonly used OECD datasets across all categories.
Inputs: no named parameters.
list_categories_detailedA
Get all OECD data categories with example datasets for each category. Returns comprehensive information about all 19 categories.
Inputs: no named parameters.
list_dataflowsA
List available OECD dataflows (datasets), optionally filtered by category. Use this to browse datasets by topic area.
Inputs: category, limit.
query_dataA
Query actual statistical data from an OECD dataset. ⚠️ IMPORTANT: Defaults to last 100 observations (max 1000) to protect context window. Use filters, time periods, or last_n_observations to control data size. Large datasets (e.g. SOCX_AGG) can have 70,000+ observations - always specify limits!
Inputs: dataflow_id (required), filter, start_period, end_period, +1 more.
search_dataflowsA
Search for OECD datasets (dataflows) by keyword. Returns matching datasets with their IDs, names, and descriptions.
Inputs: query (required), limit.
search_indicatorsB
Search for specific economic or social indicators by keyword (e.g., "inflation", "unemployment", "GDP").
Inputs: indicator (required), category.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 9 tools
Most tools have distinct purposes, but get_categories and list_categories_detailed overlap in returning category info, though descriptions differentiate by detail level. search_dataflows and list_dataflows both return datasets but use different filters.
Tools follow a consistent verb_noun pattern (e.g., get_categories, search_dataflows). Minor deviations like list_categories_detailed and get_popular_datasets include adjectives, but overall pattern is clear.
9 tools cover the full workflow of browsing, searching, getting metadata, and querying data without being excessive. Each tool serves a clear role.
The set covers all necessary operations for exploring OECD data: listing/searching datasets, retrieving metadata, querying data, and generating visualization URLs. No obvious gaps for the read-only domain.
Maintenance
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