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🪐 ✨ Earthdata MCP Server: Real-Time Analytics & Cloud Scalability - MCP Implementation

🪐 ✨ Earthdata MCP Server: Real-Time Analytics & Cloud Scalability

Earthdata MCP Server 🪐: Unleash real-time Earth analytics with cloud-powered scalability, secure global data processing, and intuitive tools for smarter environmental insights. 🌍✨

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✨ Research And Data
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About 🪐 ✨ Earthdata MCP Server

What is 🪐 ✨ Earthdata MCP Server: Real-Time Analytics & Cloud Scalability?

Earthdata MCP Server is a purpose-built Model Context Protocol (MCP) implementation designed to streamline access to NASA Earthdata repositories. It provides a robust interface for geospatial analysts to discover, retrieve, and analyze Earth observation datasets in real-time, while leveraging cloud-native scalability for high-performance workloads.

How to Use 🪐 ✨ Earthdata MCP Server: Real-Time Analytics & Cloud Scalability?

Integration with platforms like Claude Desktop requires configuring the server via Docker containers. Users should add the following configuration block to their claude_desktop_config.json file:


{
  "mcpServers": {
    "earthdata": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "datalayer/earthdata-mcp-server:latest"
      ]
    }
  }
}

For custom deployments, execute docker build -t datalayer/earthdata-mcp-server . to build from source.

🪐 ✨ Earthdata MCP Server Features

Key Features of 🪐 ✨ Earthdata MCP Server: Real-Time Analytics & Cloud Scalability?

  • Geospatial Precision: Bounding box and temporal filtering capabilities for spatial-temporal data queries
  • Efficient Discovery: Dual-search interface for both datasets (search_earth_datasets) and granules (search_earth_datagranules)
  • Cloud-Optimized: Containerized architecture enabling elastic scaling on cloud infrastructures
  • AI-Ready: Designed for seamless integration with machine learning workflows

Use Cases of 🪐 ✨ Earthdata MCP Server: Real-Time Analytics & Cloud Scalability?

Applications include:

  • Climate change impact analysis using multi-decadal satellite records
  • Disaster response teams accessing near-realtime environmental data
  • Academic research requiring high-fidelity geospatial data retrieval

See a practical example in this sea-level analysis workflow demonstration.

🪐 ✨ Earthdata MCP Server FAQ

FAQ About 🪐 ✨ Earthdata MCP Server: Real-Time Analytics & Cloud Scalability?

What data sources are supported?
All NASA Earthdata repositories including MODIS, Landsat, and SMAP datasets
Does it support serverless deployments?
Yes, through FaaS platforms via container orchestration
How are queries optimized?
Automatic parallelization of spatial queries using vectorized processing

Content

Datalayer

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🪐 ✨ Earthdata MCP Server

Github Actions Status PyPI - Version

Earthdata MCP Server is a Model Context Protocol (MCP) server implementation that provides tools to interact with NASA Earth Data. It enables efficient dataset discovery and retrieval for Geospatial analysis.

Usage with Claude Desktop

To use this with Claude Desktop, add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "earthdata": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "datalayer/earthdata-mcp-server:latest"
      ]
    }
  }
}

Components

Tools

The server currently offers 2 tools:

  1. search_earth_datasets
  • Search for datasets on NASA Earthdata.
  • Input:
    • search_keywords (str): Keywords to search for in the dataset titles.
    • count (int): Number of datasets to return.
    • temporal (tuple): (Optional) Temporal range in the format (date_from, date_to).
    • bounding_box (tuple): (Optional) Bounding box in the format (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat).
  • Returns: List of dataset abstracts.
  1. search_earth_datagranules
  • Search for data granules on NASA Earthdata.
  • Input:
    • short_name (str): Short name of the dataset.
    • count (int): Number of data granules to return.
    • temporal (tuple): (Optional) Temporal range in the format (date_from, date_to).
    • bounding_box (tuple): (Optional) Bounding box in the format (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat).
  • Returns: List of data granules.

Building from Source

docker build -t datalayer/earthdata-mcp-server .

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