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Universal Public Data MCP Server

@inamdarmihir

Repository

About Universal Public Data MCP Server

Universal Public Data MCP Server with real API integrations for financial, government, scientific, news, geographic, and technology data

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "universal-public-data-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Tools

7

`get_stock_data`, `get_crypto_data`, `get_exchange_rates`

`get_census_data`, `get_economic_indicators`, `search_sec_filings`

`get_nasa_data`, `search_research_papers`, `get_climate_data`

`get_breaking_news`, `search_news`, `analyze_media_sentiment`

`get_weather_data`, `get_air_quality`, `get_disaster_alerts`

`get_github_trends`, `get_domain_info`, `analyze_tech_trends`

`get_system_status`, `get_api_metrics`, `get_cache_stats`

Overview

What is Universal Public Data MCP Server?

A production-ready Model Context Protocol (MCP) server providing unified access to multiple public data sources including government, scientific, financial, news, and geographic APIs. Designed for enterprise deployment with comprehensive monitoring, caching, and resilience features.

How to use Universal Public Data MCP Server?

Clone the repository, create a Python virtual environment, install dependencies, and run python demo.py to verify. Configure the MCP client (e.g., Cursor IDE or Claude Desktop) with the command python src/server.py and the correct working directory. An optional config.yaml file can set cache, rate‑limit, and API key parameters.

Key features of Universal Public Data MCP Server

  • Circuit breakers for automatic failure detection and recovery
  • Multi‑tier caching with optional Redis support
  • Exponential backoff retry logic for failed requests
  • Real‑time health monitoring and performance metrics
  • Data validation and format standardization across sources
  • Docker support for simple production deployment

Use cases of Universal Public Data MCP Server

  • Retrieve real‑time stock, crypto, and exchange rate data
  • Search SEC filings, census data, and economic indicators
  • Query NASA APIs, research papers (PubMed/ArXiv), and climate data
  • Fetch breaking news, analyze media sentiment, and monitor trends
  • Get weather, air quality, and disaster alerts for a location

FAQ from Universal Public Data MCP Server

What data sources are supported?

Government (US Census, Federal Reserve, SEC, USGS), scientific (NASA, PubMed, ArXiv, NOAA), financial (Yahoo Finance, CoinGecko), news (RSS with sentiment analysis), geographic (weather, air quality, disaster alerts), and technology (GitHub trends, domain info).

How do I configure API keys?

Optional API keys for enhanced rate limits are set in the config.yaml file under the api_keys section (e.g., nasa, github).

What are the runtime requirements?

Python 3.9 or later (the Docker image uses python:3.9-slim). Redis is optional for high‑performance caching.

How can I add a new data source?

Create a new adapter in src/adapters/, register the corresponding tools in src/server.py, and add tests in the tests/ directory.

Does the server support authentication or custom transports?

The server uses the standard MCP protocol. No custom transports are documented; configuration is handled via the MCP client config and an optional config.yaml.

Frequently asked questions

What data sources are supported?

Government (US Census, Federal Reserve, SEC, USGS), scientific (NASA, PubMed, ArXiv, NOAA), financial (Yahoo Finance, CoinGecko), news (RSS with sentiment analysis), geographic (weather, air quality, disaster alerts), and technology (GitHub trends, domain info).

How do I configure API keys?

Optional API keys for enhanced rate limits are set in the `config.yaml` file under the `api_keys` section (e.g., `nasa`, `github`).

What are the runtime requirements?

Python 3.9 or later (the Docker image uses `python:3.9-slim`). Redis is optional for high‑performance caching.

How can I add a new data source?

Create a new adapter in `src/adapters/`, register the corresponding tools in `src/server.py`, and add tests in the `tests/` directory.

Does the server support authentication or custom transports?

The server uses the standard MCP protocol. No custom transports are documented; configuration is handled via the MCP client config and an optional `config.yaml`.

Comments

Basic information

Category

Data & Analytics

License

MIT license

Transports

stdio

Author

inamdarmihir

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