NREL Solar Resource MCP for AI. Check solar potential and historical radiation data for any US site.

Q: How do I get solar irradiance for a specific coordinate?

Use the getsolarresource tool by providing the latitude and longitude. The agent will return average DNI, GHI, and Tilt at Latitude data for that location.

Q: Can I search for solar data using a physical address instead of coordinates?

Yes! The querynsrdbdata tool allows you to provide an address string. The system will locate the nearest NSRDB datasets associated with that address.

Q: What types of data sources can I filter by in the NSRDB?

When using querynsrdbdata, you can use the type parameter to filter results by 'satellite' or 'station' data sources.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

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.

What your AI can do

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.

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.

Ask an AI about this

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AI Agent

NREL Solar Resource: 2 Tools for Energy Analytics

These two tools let you check immediate solar resource metrics and query vast archives of historical radiation datasets from NREL.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using NREL Solar Resource on Vinkius

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...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The NREL Solar Resource integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "nrel-solar-resource": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the NREL Solar Resource tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"nrel-solar-resource": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with NREL Solar Resource, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by NREL Solar Resource. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 2 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Finding reliable solar metrics shouldn't take an hour of clicking tabs.

Right now, assessing a site means jumping between NREL's website, reading technical manuals to find the right API endpoint, and then figuring out if you need coordinates or a bounding box. You spend more time on data *access* than on actual analysis.

With this MCP server, you just talk to your agent. Tell it the location, and tell it what metrics you want (GHI? DNI?). It handles the complex API calls for you and spits out clean numbers in seconds.

NREL Solar Resource MCP Server: Get Irradiance & Radiation Data

The manual process requires checking different data sources—one for current irradiance, another for historical radiation models. You risk missing the correlation between immediate potential and long-term degradation patterns.

Now, your agent runs both types of queries in conversation. It gives you a full picture: what the site can do today, and what it has done historically.

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What your AI can actually do with this

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.

Built · Hosted · Managed by Vinkius NREL Solar Resource - Check Irradiance & Radiation Data

Server ID 019e38ca-1a4f-70a5-9147-5efc9a78b2ee

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Compliance Grade A+

Score 100/100

Report View Report ↗

Who is this actually for?

Energy Analysts who are tired of cross-referencing multiple geospatial databases. Civil Engineers planning solar installations need reliable metrics fast. Climate Researchers can finally access historical radiation datasets without writing complex Python scripts. If your job involves site assessment, this is for you.

Energy Analyst

Uses get_solar_resource to quickly assess the solar potential of multiple geographic sites for a project feasibility report.

Civil Engineer

Retrieves precise DNI and GHI metrics needed for calculating component sizing and planning optimal panel placement.

Climate Researcher

Runs query_nsrdb_data to locate specific, historical satellite datasets required for long-term climate trend modeling.

What Changes When You Connect

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.

See it in action

01 01

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.

02 02

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.

03 03

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.

04 04

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.

The honest tradeoffs

Confusing current vs. historical data

Anti-pattern

Asking 'What is the solar resource for this location?' without specifying if you mean today's average or a 10-year trend.

The Fix

If you need immediate metrics, use get_solar_resource. If you need long-term climate context, specifically ask to run query_nsrdb_data against the NSRDB.

Relying only on general search engines

Anti-pattern

Searching Google for 'solar irradiance data' and getting 50 links that require sign-ups or complex API documentation reads.

The Fix

Use your AI agent with this server. It handles the direct connection to NREL, giving you actionable results immediately.

Forgetting coordinate formats

Anti-pattern

Providing a general city name but failing to specify the exact latitude and longitude needed for precise calculation.

The Fix

Always provide coordinates (lat/lon) when using get_solar_resource for the most accurate, immediate metrics.

When It Fits, When It Doesn't

Use this server if your project requires both a snapshot of current solar potential AND deep historical context. This combination is key. Use get_solar_resource first; it's best for initial, fast feasibility checks on DNI and GHI. If the numbers look good but you need to validate them over time (e.g., comparing decades of data), then use query_nsrdb_data. Don't just assume one tool is enough—the strength here is running both tools sequentially within your conversation flow, building a complete picture that single-source databases miss.

Questions you might have

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.

Connect to your AI in seconds.

Get solar resource

Query nsrdb data

NREL Solar Resource: 2 Tools for Energy Analytics

Make your AI actually useful.

Get Solar Resource

Query Nsrdb Data

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Finding reliable solar metrics shouldn't take an hour of clicking tabs.

NREL Solar Resource MCP Server: Get Irradiance & Radiation Data

What your AI can actually do with this

Current Site Metrics: Quick Feasibility Checks

Historical Radiation Research: Digging into NSRDB

Putting It All Together: How You'll Use This

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Initial site feasibility assessment

Long-term climate modeling

Comparing competing sites

Troubleshooting data availability

The honest tradeoffs

Confusing current vs. historical data

Relying only on general search engines

Forgetting coordinate formats

When It Fits, When It Doesn't

Questions you might have