How to Use the U.S. EIA Energy Data MCP in Pydantic AI

Q: What tools help list U.S. EIA Energy Data sources for Pydantic AI?

The listenergycategories tool lets your agent see all available data types first. You can then follow up with specific calls like listcoalproduction to get the raw metrics.

Q: Can I check regional price variations across U.S. EIA Energy Data using Pydantic AI?

Yes, the listregionalfuelprices tool is designed for this. It lets your agent identify and structure fuel price differences across various U.S. regions.

Q: What other data can I get with U.S. EIA Energy Data using Pydantic AI?

You can pull renewable stats via listrenewableenergystats, or check out specific natural gas supply details using listnaturalgasproduction. The server covers many formats.

Ensure data integrity when handling U.S. EIA Energy Data with Pydantic AI.

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Connect U.S. EIA Energy Data MCP to Pydantic AI

Create your Vinkius account to connect U.S. EIA 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 U.S. EIA Energy Data with Pydantic AI

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Validate US Energy Data using the MCP Server

The `quick_us_energy_audit` tool provides a structured, high-level summary of current U.S. energy production across all sources. Because your agent uses Pydantic validation, it knows exactly what fields to expect and can catch unexpected data types instantly. This makes the audit result reliable for mission-critical applications where silent failures aren't an option.

List Structured Energy Sources via Pydantic AI

Use `list_natural_gas_production` and `list_electricity_generation` to pull highly specific, structured data. When the API returns data, Pydantic enforces that every field—like production volume or retail sales—matches your defined schema. If a field is missing or the format is off, your agent fails loud with a clear validation error, never corrupting the output.

Retrieve Core Market Data from MCP Server

To get initial context, run `get_eia_api_metadata` to check connection parameters. Then, use `list_petroleum_prices` for structured gasoline and petroleum price data. This establishes a solid baseline of known-good data. This controlled flow ensures that the agent always works with validated inputs before proceeding to complex analysis.

Setup guide

Set up U.S. EIA 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": {
        "us-eia-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 U.S. EIA Energy Data tools.",
)

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by U.S. EIA Energy Data. 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.

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Common questions about U.S. EIA Energy Data MCP in Pydantic AI

Pydantic validates every MCP response against a strict schema at runtime. This means if the server sends unexpected data, your agent throws an explicit validation error instead of accepting bad information.

The `list_energy_categories` tool lets your agent see all available data types first. You can then follow up with specific calls like `list_coal_production` to get the raw metrics.

Yes, the `list_regional_fuel_prices` tool is designed for this. It lets your agent identify and structure fuel price differences across various U.S. regions.

You can pull renewable stats via `list_renewable_energy_stats`, or check out specific natural gas supply details using `list_natural_gas_production`. The server covers many formats.

The data includes energy metrics like time-series generation, categorized fuel prices (petroleum/gasoline), and detailed production volumes for coal and natural gas.

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