PG&E Public Datasets MCP. Model energy costs across regions and sectors.

The PG&E Public Datasets MCP Server - Energy Analysis gives your AI client direct access to PG&E's public energy metrics. You don't need an API key or complex setup; you just connect, and your agent calls the tools to pull structured data.

When you wanna look at regional differences, use compare_regions. This tool lets you compare usage figures, customer counts, and average bills across several specified ZIP codes in a single output. If you're tracking electricity consumption for multiple spots, get_electricity_by_zip pulls that data for any list of ZIP codes, breaking it down by month or year.

For natural gas, get_gas_by_zip does the same thing, retrieving consumption data for specific ZIP codes, whether you need monthly numbers or annual totals.

To analyze what's happening over time, you can run two tools: get_monthly_usage gives you detailed electric (kWh) and gas (therms) usage by a specific ZIP code, month, year, and customer segment. If you wanna spot long-term patterns instead of just monthly snapshots, get_yearly_trends tracks how overall electricity or gas usage has shifted over multiple years.

This lets you see if demand is growing steady or spiking seasonally.

For billing and cost analysis, start with get_billing_data. It retrieves average utility bills and cost metrics based on specific ZIP codes or different customer segments. You can also pinpoint consumption by type—get_usage_by_customer_type shows total electric or gas usage broken down for Residential, Commercial, Industrial, and Agricultural sectors. If you need to track what people are spending on energy efficiency, get_savings_data provides data on those program savings, including counts of participation, the saved amounts, and associated costs.

Grouping these tools together lets you build a full picture:

• You can run get_monthly_usage to get granular details for every ZIP code. Then, use compare_regions to aggregate those findings across multiple locations. You'll see the electric (kWh) and gas (therms) consumption side-by-side, along with customer counts and average bills.
• If you want to compare affordability, you can check out get_billing_data for a specific segment—say, Industrial users in ZIP 90210—and then use get_usage_by_customer_type to see if that high cost corresponds with actual usage volume.
• To study the impact of government or utility programs, you'll pair get_savings_data (showing saved amounts) with a regional comparison run using compare_regions. This lets you measure which areas got the best return on investment.

When your agent runs these tools, it gives you structured energy metrics that let you determine energy affordability and compare costs across different ZIP codes or specific customer types. You're always working with raw numbers—the full electric (kWh) and gas (therms) usage by get_usage_by_customer_type for major sectors, the historical data from get_yearly_trends, and the immediate comparison capability of compare_regions.

It’s all public data, straight to your client.