How to Use the EIA Full Access — U.S. Energy Intelligence MCP in Pydantic AI

Q: How does this setup handle large datasets like state electricity profiles?

You can use tools like getstateelectricityprofiles to pull regional data, which Pydantic AI parses into structured Python objects. If the dataset is too large, you can write agent logic to filter by specific states before validation.

Run type-safe energy market models using Pydantic AI and the EIA Full Access MCP Server.

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Connect EIA Full Access — U.S. Energy Intelligence MCP to Pydantic AI

Create your Vinkius account to connect EIA Full Access — U.S. Energy Intelligence 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 EIA Full Access — U.S. Energy Intelligence with Pydantic AI

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Perplexity

Validate petroleum stocks with strict runtime typing

`get_petroleum_stocks` pulls commercial and Strategic Petroleum Reserve inventory levels directly into your Pydantic AI agent. The framework validates every incoming integer and string against your defined schemas, preventing silent failures from malformed API payloads. If the federal endpoint returns an unexpected null value or format change, the system halts execution immediately. This strict typing ensures your automated trading models never ingest corrupted data during high-volatility market events.

Track coal prices and grades using type-safe tools

`get_coal_prices` returns regional pricing broken down by rank, including bituminous and lignite. Your Pydantic AI agent processes these structured tables, ensuring that price-by-rank fields match your internal data models exactly before running calculations. You can pair this with `get_coal_quality` to verify heat and sulfur content. Because every field is typed, your agent can safely compare coal quality metrics across different mines without risking runtime type errors.

Audit refinery operations with this validated MCP Server

`get_refinery_operations` exposes refinery capacity, utilization rates, and gross inputs. Your agent queries these metrics to monitor processing bottlenecks, relying on Pydantic's validation layer to parse complex capacity percentages. The server also exposes `get_petroleum_trade` to track imports and exports. By combining these tools, your agent can build a validated, end-to-end model of domestic fuel processing and trade flows.

Setup guide

Set up EIA Full Access — U.S. Energy Intelligence 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": {
        "eia-full-access-us-energy-intelligence-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to EIA Full Access — U.S. Energy Intelligence tools.",
)

result = await agent.run("List recent EIA Full Access — U.S. Energy Intelligence transactions")
print(result.output)

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Common questions about EIA Full Access — U.S. Energy Intelligence MCP in Pydantic AI

You install the slim package with MCP support and instantiate a unified toolset pointing to the Vinkius MCP server URL. Pass this toolset directly to your Agent instance, and the framework will handle tool discovery and type-safe schema validation automatically.

The framework will raise a validation error at runtime, preventing your agent from passing bad data to the LLM. This is critical for energy applications where a misplaced decimal point in crude production figures could ruin downstream calculations.

Yes, because Pydantic AI is model-agnostic, you can connect this MCP server to local models or any major cloud provider. The framework translates the thirty-four energy tool schemas into the format required by your chosen model.

You can use tools like `get_state_electricity_profiles` to pull regional data, which Pydantic AI parses into structured Python objects. If the dataset is too large, you can write agent logic to filter by specific states before validation.

Your queries for refinery utilization, coal prices, and petroleum stocks are sent over secure transport layers directly to a V8 isolate sandbox. Vinkius handles the federal API authentication securely and destroys the processing container as soon as the tool execution completes.

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