PG&E Public Datasets MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect PG&E Public Datasets through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"pge-public-datasets": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using PG&E Public Datasets, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About PG&E Public Datasets MCP Server
Access PG&E Public Datasets directly from any AI agent and explore energy consumption, billing trends, efficiency savings, and regional comparisons without any authentication.
LangChain's ecosystem of 500+ components combines seamlessly with PG&E Public Datasets through native MCP adapters. Connect 8 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Monthly Usage — Get monthly electricity and gas consumption by ZIP code and customer segment
- Customer Segments — View energy usage breakdown across Residential, Commercial, Industrial, and Agricultural sectors
- Yearly Trends — Analyze year-over-year energy consumption trends
- Electricity by ZIP — Access ZIP code-level electricity consumption data
- Gas by ZIP — Access ZIP code-level natural gas consumption data
- Billing Data — Retrieve average bills and cost metrics by region
- Savings Data — Analyze energy efficiency program savings and cost-effectiveness
- Regional Comparisons — Compare energy usage across multiple ZIP codes side-by-side
The PG&E Public Datasets MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect PG&E Public Datasets to LangChain via MCP
Follow these steps to integrate the PG&E Public Datasets MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from PG&E Public Datasets via MCP
Why Use LangChain with the PG&E Public Datasets MCP Server
LangChain provides unique advantages when paired with PG&E Public Datasets through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine PG&E Public Datasets MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across PG&E Public Datasets queries for multi-turn workflows
PG&E Public Datasets + LangChain Use Cases
Practical scenarios where LangChain combined with the PG&E Public Datasets MCP Server delivers measurable value.
RAG with live data: combine PG&E Public Datasets tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query PG&E Public Datasets, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain PG&E Public Datasets tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every PG&E Public Datasets tool call, measure latency, and optimize your agent's performance
PG&E Public Datasets MCP Tools for LangChain (8)
These 8 tools become available when you connect PG&E Public Datasets to LangChain via MCP:
compare_regions
Returns side-by-side usage figures (kWh and therms), customer counts, and average bills for each region. Use this to identify regional differences in energy consumption, support geographic targeting for efficiency programs, or compare urban vs. rural usage patterns. ZIP codes are comma-separated (e.g., "94102,94103,94104"). Optional dataType and year filters. Compare energy usage data between multiple ZIP codes/regions
get_billing_data
Data is available by ZIP code and customer segment. Use this to analyze energy affordability, compare costs across regions, or identify rate impact on customers. Optional zipCode and year filters. Get billing data and average costs from PG&E public datasets
get_electricity_by_zip
Returns monthly or annual usage figures broken down by geographic area. Use this to compare electricity usage across neighborhoods, identify high-consumption areas, or support energy efficiency targeting. Optional year filter. Get electricity consumption data for specific ZIP codes in PG&E service area
get_gas_by_zip
Returns monthly or annual gas usage figures by geographic area. Use this to analyze heating demand patterns, compare gas usage across regions, or identify electrification opportunities. Optional year filter. Get natural gas consumption data for specific ZIP codes in PG&E service area
get_monthly_usage
Data is organized by ZIP code, month, year, and customer segment (Residential, Commercial, Industrial, Agricultural). Returns kWh for electric and therms for gas. Use this to analyze consumption patterns over time. Optional filters: dataType ("electric" or "gas"), customerType, zipCode (5-digit), and year (YYYY). Get monthly energy consumption data by ZIP code and customer segment from PG&E public datasets
get_savings_data
Includes program participation counts, kWh/therms saved, program costs, and cost-effectiveness metrics by program type. Use this to evaluate program ROI, compare effectiveness across initiatives, or identify high-impact efficiency strategies. Optional programType and year filters. Get energy efficiency program savings data from PG&E
get_usage_by_customer_type
Shows total consumption for Residential, Commercial, Industrial, and Agricultural sectors. Use this to understand the energy consumption distribution across different customer categories. Optional dataType ("electric"/"gas") and year filters. Get energy usage broken down by customer segment (residential, commercial, industrial, agricultural)
get_yearly_trends
Shows how electricity and gas usage has changed over multiple years. Use this to identify long-term patterns, growth/decline in energy demand, and seasonal variations. Optional dataType filter ("electric" or "gas"). Get yearly energy consumption trends from PG&E public data
Example Prompts for PG&E Public Datasets in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with PG&E Public Datasets immediately.
"Show monthly electricity usage by customer type."
"Compare ZIP codes 94102, 94301, and 95054."
"Show yearly energy consumption trends."
Troubleshooting PG&E Public Datasets MCP Server with LangChain
Common issues when connecting PG&E Public Datasets to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersPG&E Public Datasets + LangChain FAQ
Common questions about integrating PG&E Public Datasets MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect PG&E Public Datasets with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect PG&E Public Datasets to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.