How to Use the NREL Energy Data MCP in Pydantic AI

Get type-safe NREL Energy Data in Pydantic AI for reliable agent decisions.

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Connect NREL Energy Data MCP to Pydantic AI

Create your Vinkius account to connect NREL Energy Data to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup NREL Energy Data with Pydantic AI

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Perplexity

Strict type-safe tool responses

Pydantic AI validates every response from the NREL tools against your models. If `get_utility_rates` returns malformed data, the agent fails immediately to prevent logic errors. This ensures your agent works with clean, predictable data. You catch issues at the boundary instead of debugging downstream hallucinations.

Validated site data retrieval

Use `list_transportation_incentives` to get structured financial data for your agent. The framework enforces your schema requirements on every incoming record. This eliminates the need for manual parsing or custom validation code. Your agent receives ready-to-use objects that adhere to your project standards.

Reliable solar data integration

Query `get_solar_resource` with full confidence in the data types. Your agent processes the radiation metrics according to the exact schema you defined for your models. This creates a robust link between the NREL API and your agent's internal logic. You get runtime safety for every single energy calculation.

Setup guide

Set up NREL Energy Data MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "nrel-energy-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to NREL Energy Data tools.",
)

result = await agent.run("List recent NREL Energy Data transactions")
print(result.output)

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Common questions about NREL Energy Data MCP in Pydantic AI

You define your Pydantic models for the tool outputs. The framework automatically validates every server response against these models before the agent processes them.

The agent will trigger a validation error. This prevents corrupted data from entering your reasoning loop, keeping your agent's state clean.

Yes. Since the framework is model-agnostic, you can swap between OpenAI, Anthropic, or local models while keeping your NREL tool definitions identical.

The server runs externally and communicates over HTTP or SSE. This keeps your agent logic decoupled from the data provider's infrastructure.

Your queries stay private through token-based authentication. The server processes your requests for fuel or utility data without storing any of your operational parameters.

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