hanlulong/openecon-data — Give your AI agent accurate economic data.

Description and use

**Brief Summary (2‑3 sentences)**
hanlulong/openecon-data is an open‑source MCP (Model Context Protocol) server that aggregates more than 330 K economic indicators from sources such as FRED, the World Bank, IMF, and Eurostat, and exposes them through a unified API and a web UI. It enables AI agents to retrieve accurate, up‑to‑date macro‑economic data via a standard protocol, making it easy to plug real‑world knowledge into conversational or autonomous systems. With a Python codebase, recent commits, and modest community interest, it is positioned as a production‑ready component for AI‑driven economic analytics.

**Value**

• **Accurate, wide‑coverage data**: By consolidating dozens of reputable statistical agencies, the project eliminates the need for developers to integrate each source separately.
• **Standardized access**: The MCP interface and accompanying SDK/CLI let AI models query data in a consistent way, reducing integration friction and improving reproducibility.
• **Rapid prototyping & deployment**: The built‑in web UI lets non‑technical users explore the dataset and test queries before embedding the service into larger pipelines.

**Practical Adoption Path**

1. **Evaluate the API/SDK** – Clone the repo, spin up the Docker‑compose or pip install the Python package, and run a few sample queries against the MCP endpoint.
2. **Integrate with your AI agent** – Use the provided client library (or a simple HTTP call) to fetch indicators inside the agent’s prompt or tool‑use logic.
3. **Customize data sources** – If additional datasets are needed, extend the ingestion pipeline (Python scripts) to pull new CSV/JSON feeds and register them in the MCP catalog.
4. **Deploy to production** – Deploy the MCP server behind a reverse proxy, enable TLS, and configure rate‑limiting. Optionally containerize it for Kubernetes or serverless environments.

**Production Readiness**

• **Activity & maintenance**: The repository shows recent commits (as of 2026‑05‑12) and a modest but active community (38 stars, 8 forks).
• **Technology stack**: Pure Python with clear dependency management, making it easy to audit and containerize.
• **Security & licensing**: No obvious metadata risks, but a final review of the open‑source license and vulnerability scanning of dependencies is advisable.
• **Scalability**: The MCP server is designed to serve high‑volume queries; performance can be tuned via caching layers or database back‑ends.

Overall, hanlulong/openecon-data offers a solid, production‑grade foundation for enriching AI assistants with trustworthy economic data, and its straightforward API and deployment model make it ready for pilot projects and larger‑scale deployments after a brief security and licensing audit.

Problem

hanlulong/openecon-data helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

Give your AI agent accurate economic data. 330K indicators from FRED, World Bank, IMF, Eurostat & more. MCP server + web UI.

Source