NASA MCP Server

What does “NASA” MCP Server do?

The NASA MCP Server is a Model Context Protocol (MCP) server that provides a standardized interface for AI models to interact with NASA’s vast array of data sources. By connecting to over 20 NASA data sources through a single, consistent interface, AI assistants and developers can easily retrieve, process, and manage NASA data without needing to handle the complexities of multiple APIs. The server offers standardized data formats optimized for AI consumption, automatic parameter validation, rate limit management, and support for various imagery formats. This powerful tool enables AI agents to perform tasks such as querying astronomical data, accessing Earth imagery, managing scientific datasets, and more, thereby enhancing developer workflows for research, education, and exploration.

List of Prompts

No information about reusable prompt templates is available in the repository.

List of Resources

• APOD (Astronomy Picture of the Day): Provides daily astronomical images and related metadata from NASA.
• EPIC (Earth Polychromatic Imaging Camera): Offers satellite imagery of Earth, capturing atmospheric and surface phenomena.
• NASA Open API: General gateway to various NASA datasets, enabling access to public scientific and imagery data.

List of Tools

No explicit tool definitions found in the available files (such as server.py or similar). The server’s tools are likely the API endpoints to NASA resources, such as fetching APOD or EPIC data, but no formal tool primitives are documented.

Use Cases of this MCP Server

• Astronomy Education: Teachers and students can easily fetch daily space images and explanations using the APOD resource, making astronomy lessons more interactive.
• Earth Monitoring: Researchers can access up-to-date EPIC satellite imagery for studying weather and environmental changes.
• Space Data Exploration: Developers can build applications that surface NASA’s open data, such as mission details, satellite positions, or image galleries, through a unified interface.
• AI Model Training: Data scientists can collect a wide range of labeled NASA datasets for training machine learning models in space science or earth observation.
• Automated Reporting: AI assistants can automatically generate reports or summaries about current astronomical events using NASA’s up-to-date data sources.

How to set it up

Windsurf

No setup instructions found for Windsurf.

Claude

No setup instructions found for Claude.

Cursor

1. Ensure you are using Cursor version 0.45.6+.
2. Obtain your NASA API key from https://api.nasa.gov/.
3. Create or edit an mcp.json file in your Cursor configuration directory.
4. Add the following JSON snippet:

   {
     "mcpServers": {
       "nasa-mcp": {
         "command": "npx",
         "args": ["-y", "@programcomputer/nasa-mcp-server@latest"],
         "env": {
           "NASA_API_KEY": "your-api-key"
         }
       }
     }
   }
   

5. Restart Cursor to load the new MCP server and tools.

Securing API Keys

Use environment variables in your configuration:

"env": {
  "NASA_API_KEY": "your-api-key"
}

Replace "your-api-key" with your actual NASA API key.

Cline

No setup instructions found for Cline.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "nasa-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “nasa-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The NASA MCP Server offers a solid bridge between AI agents and NASA’s open data, with clear setup for Cursor and good documentation of its features and resources. However, it lacks detailed documentation on prompt templates and formal tool primitives, and doesn’t mention advanced MCP features like roots or sampling. For developers interested in NASA data, it provides value, but would benefit from expanded MCP-native documentation and multi-platform instructions.

MCP Score

Overall rating: 6/10 — Great for NASA data and easy Cursor setup, but missing MCP advanced features and clear tool definitions.