NREL Solar Resource MCP Server for LlamaIndexGive LlamaIndex instant access to 2 tools to Get Solar Resource and Query Nsrdb Data
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add NREL Solar Resource as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The NREL Solar Resource MCP Server for LlamaIndex is a standout in the Data Analytics category — giving your AI agent 2 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to NREL Solar Resource. "
"You have 2 tools available."
),
)
response = await agent.run(
"What tools are available in NREL Solar Resource?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About NREL Solar Resource MCP Server
Connect your AI agent to the National Renewable Energy Laboratory (NREL) Solar Resource API to analyze solar potential and access historical radiation data through natural conversation.
LlamaIndex agents combine NREL Solar Resource tool responses with indexed documents for comprehensive, grounded answers. Connect 2 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Solar Irradiance — Retrieve average solar irradiance data including Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), and Tilt at Latitude for specific coordinates.
- NSRDB Queries — Search the National Solar Radiation Database for the nearest datasets based on latitude/longitude, address, or Well-Known Text (WKT) geometry.
- Data Sourcing — Identify specific satellite or station-based datasets for renewable energy research and site assessment.
The NREL Solar Resource MCP Server exposes 2 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 2 NREL Solar Resource tools available for LlamaIndex
When LlamaIndex connects to NREL Solar Resource through Vinkius, your AI agent gets direct access to every tool listed below — spanning solar-irradiance, renewable-energy, climate-data, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Get solar resource on NREL Solar Resource
Get average solar irradiance data for a location
Query nsrdb data on NREL Solar Resource
Query nearest NSRDB datasets for a location
Connect NREL Solar Resource to LlamaIndex via MCP
Follow these steps to wire NREL Solar Resource into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the NREL Solar Resource MCP Server
LlamaIndex provides unique advantages when paired with NREL Solar Resource through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine NREL Solar Resource tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain NREL Solar Resource tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query NREL Solar Resource, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what NREL Solar Resource tools were called, what data was returned, and how it influenced the final answer
NREL Solar Resource + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the NREL Solar Resource MCP Server delivers measurable value.
Hybrid search: combine NREL Solar Resource real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query NREL Solar Resource to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying NREL Solar Resource for fresh data
Analytical workflows: chain NREL Solar Resource queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for NREL Solar Resource in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with NREL Solar Resource immediately.
"What is the average solar irradiance for latitude 34.05 and longitude -118.24?"
"Find the nearest NSRDB datasets for 'Golden, Colorado'."
"Query satellite-based NSRDB data for the coordinates 40.71, -74.00."
Troubleshooting NREL Solar Resource MCP Server with LlamaIndex
Common issues when connecting NREL Solar Resource to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpNREL Solar Resource + LlamaIndex FAQ
Common questions about integrating NREL Solar Resource MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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