PG&E Public Datasets MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PG&E Public Datasets as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to PG&E Public Datasets. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in PG&E Public Datasets?"
)
print(response)
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.
LlamaIndex agents combine PG&E Public Datasets tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the PG&E Public Datasets MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from PG&E Public Datasets
Why Use LlamaIndex with the PG&E Public Datasets MCP Server
LlamaIndex provides unique advantages when paired with PG&E Public Datasets through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PG&E Public Datasets tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PG&E Public Datasets tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PG&E Public Datasets, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PG&E Public Datasets tools were called, what data was returned, and how it influenced the final answer
PG&E Public Datasets + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PG&E Public Datasets MCP Server delivers measurable value.
Hybrid search: combine PG&E Public Datasets real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PG&E Public Datasets to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying PG&E Public Datasets for fresh data
Analytical workflows: chain PG&E Public Datasets queries with LlamaIndex's data connectors to build multi-source analytical reports
PG&E Public Datasets MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect PG&E Public Datasets to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting PG&E Public Datasets to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPG&E Public Datasets + LlamaIndex FAQ
Common questions about integrating PG&E Public Datasets MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.