# NREL Solar Resource MCP

> NREL Solar Resource connects your AI agent to NREL's solar data APIs. It lets you check average solar irradiance (DNI/GHI) and search the National Solar Radiation Database (NSRDB) for historical datasets across any US location, all without manual API calls.

## Overview
- **Category:** data-analytics
- **Price:** Free
- **Tags:** solar-irradiance, renewable-energy, climate-data, geospatial-data, environmental-science, historical-data

## Description

Hey, check this out. NREL Solar Resource hooks up your agent straight to the National Renewable Energy Laboratory's solar data APIs. You can pull historical radiation metrics and figure out a site’s solar potential just by talking to your agent. No manual API calls, no headache.

### Current Site Metrics: Quick Feasibility Checks

When you need to know what the sun's doing *right now* at a specific spot, the `get_solar_resource` tool handles it. You just feed it coordinates, and it hands back crucial average solar irradiance data—specifically Direct Normal Irradiance (DNI) and Global Horizontal Irradiance (GHI). DNI tells you how much direct sunlight hits perpendicular to the surface; GHI gives you a broader view of all the incoming radiation on a horizontal plane. It also spits out the Tilt at Latitude, which is key for understanding panel mounting angles.

This capability lets you assess project feasibility fast. You don't gotta spend hours cross-referencing spreadsheets or building complex API calls just to get these core numbers. If you need to run a quick check on whether a site has enough potential solar power, your agent handles the data retrieval instantly and keeps it right in your conversation flow.

If you're comparing multiple sites—say, checking out three different land parcels for an array installation—you can use the same resource. You just ask your agent to pull DNI and GHI for each set of coordinates, letting you compare these specific irradiance data points side-by-side without ever leaving the chat interface.

### Historical Radiation Research: Digging into NSRDB

When quick metrics aren't enough, you gotta look at history. That’s where the `query_nsrdb_data` tool comes in, giving you access to the massive National Solar Radiation Database (NSRDB). This isn't just a lookup; it lets your agent search for long-term historical datasets across any US location.

You don't need to know complex geospatial jargon. You can tell your agent an address, and it handles finding nearby relevant NSRDB data using that address. Alternatively, if you have precise latitude/longitude coordinates or even a WKT geometry boundary, the tool queries those inputs directly to locate matching historical datasets for deep research.

This is essential when you're working on climate models or long-term energy studies. Instead of spending days manually checking documentation to figure out which satellite model or ground station holds the data you need, your agent does the heavy lifting. It finds the right dataset within NSRDB based on the location parameters you provide.

### Putting It All Together: How You'll Use This

*   **Assessing Viability:** Need to know if a new farm site is viable? Give it coordinates; your agent pulls DNI, GHI, and Tilt. That's your initial go/no-go signal.
*   **Academic Deep Dive:** Working on historical energy trends? You feed the tool an address or bounding box, and it zeroes in on decades of radiation data from NSRDB for academic rigor.
*   **Comparative Analysis:** Comparing Site A vs. Site B? Just ask your agent to get solar metrics for both locations sequentially. It keeps those DNI/GHI numbers clean so you can compare resource quality directly.

It's straightforward, man. You talk to the agent, it hits NREL’s APIs, and you get clean, actionable data on sun potential or decades of historical radiation—all without writing a single line of Python.

## Tools

### get_solar_resource
Gets average solar irradiance data (DNI, GHI) and Tilt at Latitude for a specific location's coordinates.

### query_nsrdb_data
Queries the National Solar Radiation Database (NSRDB) to find nearby historical datasets using latitude/longitude or an address.

## Prompt Examples

**Prompt:** 
```
What is the average solar irradiance for latitude 34.05 and longitude -118.24?
```

**Response:** 
```
I've retrieved the solar resource data for those coordinates. The average Direct Normal Irradiance (DNI) is 6.24 kWh/m²/day, and the Global Horizontal Irradiance (GHI) is 5.12 kWh/m²/day.
```

**Prompt:** 
```
Find the nearest NSRDB datasets for 'Golden, Colorado'.
```

**Response:** 
```
Searching NSRDB for Golden, CO... I found several datasets. The closest station is located at 39.74, -105.17 with data available from the PSM V3 satellite model. Would you like the download links?
```

**Prompt:** 
```
Query satellite-based NSRDB data for the coordinates 40.71, -74.00.
```

**Response:** 
```
I've filtered the NSRDB for satellite datasets at those coordinates. I found a high-resolution satellite dataset (PSM) covering that area. It includes 30-minute interval data for solar radiation and meteorological variables.
```

## Capabilities

### Check current solar metrics
The agent retrieves average Direct Normal Irradiance (DNI) and Global Horizontal Irradiance (GHI) for a given location.

### Search historical radiation datasets
The agent searches the NSRDB using coordinates, addresses, or WKT geometry to find relevant long-term data sets.

### Assess project feasibility quickly
You can run a quick check on a site's solar potential metrics directly in your conversation flow.

### Compare different resource types
The agent pulls specific irradiance data points (DNI, GHI) so you can compare them across multiple sites.

### Find matching datasets for research
When working on climate or energy studies, the agent locates the right NSRDB dataset without needing manual API calls.

## Use Cases

### Initial site feasibility assessment
A project manager needs to know if a new development in Denver, CO, can support solar power. Instead of opening three different tabs and running manual API calls for DNI/GHI, they ask their agent: 'What is the average solar irradiance at 39.74, -105.17?' The agent uses `get_solar_resource` and spits out the numbers in seconds.

### Long-term climate modeling
A researcher is studying how solar resources changed over decades at a specific coastal point. They use `query_nsrdb_data`, providing only the rough coordinates, and the agent finds multiple relevant NSRDB datasets from various satellite models (like PSM V3) for deep analysis.

### Comparing competing sites
An engineer must decide between two potential installation spots. They ask their agent to run `get_solar_resource` on both sets of coordinates, allowing them to compare DNI and GHI side-by-side within the same chat session.

### Troubleshooting data availability
You are writing a paper that requires data from a specific geographic region. Instead of guessing which dataset is right, you use `query_nsrdb_data` to search by address or WKT geometry and let the server pinpoint the exact historical records.

## Benefits

- Real-time metrics instantly. Instead of manually calling the NREL API to check DNI or GHI, you just ask your agent with `get_solar_resource`. It gives you the average irradiance numbers immediately.
- Deep historical context. Need to know what the weather was like five years ago? Use `query_nsrdb_data` to search the NSRDB for long-term climate and radiation trends, bypassing manual database searches.
- Reduced data sourcing time. You don't have to figure out if you need satellite or station data; the server helps identify specific datasets needed for your research project.
- Focus on what matters. By using this single MCP interface, you keep all your solar resource checks—from current irradiance to historical records—in one conversation thread.
- Better planning decisions. You get precise metrics that let you move past 'maybe' and start making engineering estimates based on solid data.

## How It Works

The bottom line is: you talk naturally, your agent does the data heavy lifting using NREL's established APIs.

1. First, subscribe to this server and enter your NREL API Key. This authenticates access to the live data feeds.
2. Next, tell your AI client what you need—for instance: 'What's the GHI at 34.05, -118.24?'
3. The agent calls the appropriate tool, sends the coordinates to NREL, and returns a clean readout of the requested solar metrics or dataset links.

## Frequently Asked Questions

**How do I get solar irradiance for a specific coordinate?**
Use the `get_solar_resource` tool by providing the latitude and longitude. The agent will return average DNI, GHI, and Tilt at Latitude data for that location.

**Can I search for solar data using a physical address instead of coordinates?**
Yes! The `query_nsrdb_data` tool allows you to provide an `address` string. The system will locate the nearest NSRDB datasets associated with that address.

**What types of data sources can I filter by in the NSRDB?**
When using `query_nsrdb_data`, you can use the `type` parameter to filter results by 'satellite' or 'station' data sources.

**How do I start using the `get_solar_resource` tool?**
You must first subscribe to the NREL Solar Resource server on Vinkius. After subscribing, you'll need to provide your specific NREL API Key for authentication.

**Does `query_nsrdb_data` support complex geometry inputs?**
Yes, it handles more than just simple coordinates. You can pass Well-Known Text (WKT) geometry to define a specific area of interest for the search.

**What metrics does `get_solar_resource` return, and what are their units?**
It returns key metrics like Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), and Tilt at Latitude. These values are typically provided in kWh/m²/day.

**Are there any rate limits I should know about when using this server?**
Yes, the NREL API has usage restrictions. We recommend checking Vinkius's documentation or the official NREL guidelines to keep your calls within acceptable limits.

**Can `query_nsrdb_data` search for both satellite and station datasets?**
It finds both types of data. The tool searches across various sources, allowing you to compare models from specific stations or high-resolution satellite coverage.