# PG&E Public Datasets MCP for AI Agents AI Agent Connect > PG&E Public Datasets MCP gives you direct access to utility data like monthly energy usage, billing trends, and regional comparisons. It lets your AI client pull ZIP code-level electricity and gas data without needing any API keys or complex authentication. It's built for anyone needing real-world energy metrics for research, policy analysis, or market targeting. ## Overview - **Category:** data-analytics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_jU4H9PpngMJSWTMWiUpgHMUI2qIOmttvQ6Nykwgk/ai-agent-connect - **Tags:** energy-consumption, billing-trends, efficiency-savings, public-data, utility-data ## Description Imagine you're trying to figure out which neighborhoods in California are the best targets for new solar installations or why electricity costs are spiking in a specific ZIP code. Usually, this means digging through messy public PDFs or trying to scrape outdated tables. With this Connector, you just ask your agent to pull the numbers. You get straight to the facts on monthly consumption, average billing costs, and how different customer segments like industrial versus residential are using power. It handles the heavy lifting of organizing these datasets so you can focus on the analysis. Since it's hosted on Vinkius, you can plug it into your existing workflow and start querying PG&E data immediately. Whether you're looking for year-over-year trends or comparing energy efficiency savings across different regions, the data is ready for you. No more manual data entry or hunting for links. You get a clean, structured view of the energy landscape without the headache of data cleaning or authentication hurdles. ## Tools ### compare_regions Get side-by-side figures for multiple ZIP codes to identify regional differences in usage and costs. ### get_electricity_by_zip Fetch monthly or annual electricity figures for specific neighborhoods or geographic areas. ### get_gas_by_zip Retrieve natural gas usage data to analyze heating demand or identify electrification opportunities. ### get_monthly_usage Get detailed monthly consumption figures broken down by ZIP code and customer segment. ### get_savings_data Access metrics on energy efficiency program participation, costs, and total savings. ### get_billing_data Pull average costs and billing metrics by region or ZIP code to see how energy prices affect different customers. ### get_usage_by_customer_type Breakdown total energy use for residential, commercial, industrial, and agricultural sectors. ### get_yearly_trends See how electricity and gas demand has shifted over several years to spot long-term growth. ## Prompt Examples **Prompt:** ``` What's the average bill like in Palo Alto compared to San Jose? ``` **Response:** ``` ### Regional Billing Comparison | Location | Avg. Bill | Monthly Usage | Customer Count | | :--- | :--- | :--- | :--- | | **Palo Alto (94301)** | $165 | 4.8M kWh | 28,000 | | **San Jose (95112)** | $142 | 5.5M kWh | 34,000 | **Key Takeaway:** Palo Alto shows a significantly higher per-customer average bill, likely reflecting higher income brackets and larger home sizes compared to the San Jose average. ``` **Prompt:** ``` Show me the electricity usage for residential vs industrial types. ``` **Response:** ``` ### Energy Usage by Customer Segment Based on the latest public data, here is how energy demand is distributed: * **Residential:** ~650 kWh/month (Avg) * **Commercial:** ~4,200 kWh/month (Avg) * **Industrial:** ~45,000 kWh/month (Avg) * **Agricultural:** ~12,000 kWh/month (Avg) **Analysis:** Industrial customers account for the highest volume of consumption, while residential usage remains the most consistent across urban areas. ``` **Prompt:** ``` Give me a summary of energy savings from last year's programs. ``` **Response:** ``` ### Energy Efficiency Program Results Last year's efficiency initiatives showed significant impact: * **Total Savings:** 1.2M kWh saved across all programs. * **Participation:** 15,000+ households joined the rebate initiatives. * **Cost-Effectiveness:** The heat pump adoption program showed a 15% higher ROI than standard insulation rebates. **Summary:** Programs targeting high-consumption residential areas yielded the best cost-per-kWh savings. ``` ## Capabilities ### Pull ZIP code electricity data Get monthly or annual electricity usage figures for specific geographic areas. ### Compare regional energy usage View side-by-side usage and billing figures for multiple ZIP codes at once. ### Fetch billing and cost metrics Retrieve average costs and billing data by region or customer segment. ### Analyze yearly consumption trends Identify long-term patterns in electricity and gas demand over multiple years. ### View customer segment breakdowns See the distribution of energy use across residential, commercial, and industrial sectors. ### Get energy efficiency savings Access data on program participation, costs, and total energy saved. ## Use Cases ### Solar Sales Targeting A sales lead asks the agent to find high-consumption ZIP codes to build a lead list for new solar installations. ### Policy Analysis A researcher uses compare_regions to see if rural areas face higher energy costs than urban centers to draft a report. ### Trend Reporting A journalist asks for yearly trends to write a story on the growth of data center energy use in California. ### Efficiency Audits A consultant uses get_savings_data to see which energy programs actually move the needle on costs for customers. ## Benefits - Skip the manual PDF hunt by using get_electricity_by_zip to pull neighborhood data instantly. - Identify high-value targets for solar sales by using compare_regions to spot high-usage areas. - Analyze energy policy impact using get_billing_data to see how rates affect different demographics. - Track long-term infrastructure needs with get_yearly_trends to see demand shifts over time. - Evaluate program ROI for clean energy initiatives using get_savings_data to see real-world impact. ## How It Works The bottom line is you get instant access to public utility data without handling any API keys or authentication. 1. Subscribe to the PG&E Public Datasets MCP on Vinkius. 2. Connect your AI client to the Connector. 3. Ask your agent to pull specific energy metrics or regional comparisons. ## Frequently Asked Questions **Can I use the PG&E Public Datasets MCP to find high-usage neighborhoods?** Yes, you can use it to identify specific areas with high electricity demand by pulling data for various ZIP codes. **Does the PG&E Public Datasets MCP require an API key?** No, it's completely free and doesn't require any authentication or API keys to use. **Can I compare energy costs between different ZIP codes with this?** Yes, you can pull side-by-side billing and usage data for multiple regions to see how costs differ. **Is the PG&E Public Datasets MCP good for solar sales research?** It's great for identifying high-consumption areas to target for solar products or other clean energy services. **Does this Connector show industrial vs residential energy use?** Yes, it breaks down consumption by residential, commercial, industrial, and agricultural categories. **Can I see how much energy was saved by efficiency programs?** Yes, you can access data on program participation and total savings to see the impact of various initiatives. **Is any authentication required?** No! All PG&E Public Datasets are completely free and accessible without any API key or authentication. Just subscribe and start querying energy data immediately. **What customer segments are available?** PG&E provides data for four customer segments: Residential (homes), Commercial (businesses), Industrial (manufacturing), and Agricultural (farming). Each segment has different consumption patterns and billing structures. **Can I compare multiple ZIP codes?** Yes! Use the compare_regions tool with comma-separated ZIP codes (e.g., "94102,94103,94104"). It returns side-by-side usage data, customer counts, and average bills for each region, making it easy to identify geographic differences in energy consumption.