How to Use the EIA Natural Gas — Gas Market Intelligence MCP in Pydantic AI

Build type-safe gas market workflows in Pydantic AI with runtime validation for every EIA storage and price data point.

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Connect EIA Natural Gas — Gas Market Intelligence MCP to Pydantic AI

Create your Vinkius account to connect EIA Natural Gas — Gas Market 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 Natural Gas — Gas Market Intelligence with Pydantic AI

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Validate weekly storage reports with Pydantic AI

The `get_natgas_storage` tool delivers the latest weekly underground storage levels directly to your agent. Since you are using Pydantic AI, every Bcf value and date string is validated against strict schemas at runtime. If the EIA API returns unexpected formatting, the framework raises an immediate validation error instead of letting your trading model process corrupted data. This type safety is critical when automating high-stakes financial decisions based on the weekly storage report.

Enforce schema validation on Henry Hub spot prices

The `get_natgas_prices` tool pulls benchmark Henry Hub spot and futures data, along with industrial and residential rates. Your agent parses these prices using type-safe models to ensure calculations like basis spreads are mathematically sound. This setup prevents silent failures in your pricing pipelines. By routing the MCP server through Pydantic AI, you guarantee that every spot price used in your trading logic matches the expected float format.

Run verified supply-demand models using this MCP Server

The `get_natgas_summary` tool aggregates total production, consumption, trade, and storage changes into a single market balance overview. Your agent processes this summary to determine if the physical market is tightening or loosening. For deeper analysis, the agent can call `get_natgas_production` and `get_natgas_consumption` to verify state-level details. Every field returned by these tools is strictly typed, giving your data pipeline the reliability needed for production environments.

Setup guide

Set up EIA Natural Gas — Gas Market 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-natural-gas-gas-market-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 Natural Gas — Gas Market Intelligence tools.",
)

result = await agent.run("List recent EIA Natural Gas — Gas Market Intelligence transactions")
print(result.output)

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Common questions about EIA Natural Gas — Gas Market Intelligence MCP in Pydantic AI

Use the MCPToolset class with the server's HTTP endpoint. Pass this toolset directly to your Agent constructor to expose tools like `get_natgas_prices` and `get_natgas_storage` to your model.

The framework immediately throws a validation error, preventing your agent from executing trades based on bad data. This ensures your pipeline fails loudly rather than processing corrupted price or storage figures.

Yes, you can run the server locally and connect using the unified MCPToolset interface. Pydantic AI supports both Streamable HTTP and SSE transports for external server connections.

Yes, this configuration is completely model-agnostic. You can use it with Anthropic, Gemini, or local models, and the framework will still enforce strict schema validation on all returned gas data.

The server processes all EIA consumption and storage metrics within an ephemeral, zero-trust sandbox. No query history or API keys are stored on disk, ensuring your operational intelligence and trading queries remain completely private.

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