PG&E Data Portals MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PG&E Data Portals as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Data Portals. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in PG&E Data Portals?"
)
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 Data Portals MCP Server
Connect PG&E Data Portals to any AI agent and programmatically search, discover, and query PG&E's public energy datasets through natural conversation.
LlamaIndex agents combine PG&E Data Portals tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Dataset Search — Search the complete PG&E Data Portals catalog for energy-related datasets
- Energy Usage — Query electricity and gas consumption data by ZIP code and date range
- EV Adoption — Access electric vehicle registration and adoption trends by geographic area
- Solar Generation — Retrieve solar energy production and net energy metering (NEM) statistics
- Energy Efficiency — Analyze program participation, energy savings achieved, and cost-effectiveness
- Grid Infrastructure — Access distribution circuit, substation, and grid capacity data
- Date Range Queries — Filter any dataset by specific time periods for trend analysis
- Dataset Metadata — Get schema information and field descriptions for all datasets
The PG&E Data Portals MCP Server exposes 10 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 Data Portals to LlamaIndex via MCP
Follow these steps to integrate the PG&E Data Portals 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 10 tools from PG&E Data Portals
Why Use LlamaIndex with the PG&E Data Portals MCP Server
LlamaIndex provides unique advantages when paired with PG&E Data Portals through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PG&E Data Portals tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PG&E Data Portals tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PG&E Data Portals, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PG&E Data Portals tools were called, what data was returned, and how it influenced the final answer
PG&E Data Portals + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PG&E Data Portals MCP Server delivers measurable value.
Hybrid search: combine PG&E Data Portals real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PG&E Data Portals 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 Data Portals for fresh data
Analytical workflows: chain PG&E Data Portals queries with LlamaIndex's data connectors to build multi-source analytical reports
PG&E Data Portals MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect PG&E Data Portals to LlamaIndex via MCP:
get_dataset_schema
Use this to understand what columns and data types are available before querying. The datasetId is obtained from search_datasets or list_all_datasets. Get the schema/metadata for a specific PG&E dataset
list_all_datasets
Each dataset includes name, description, ID, and metadata. Use this as a starting point to explore what data is available from PG&E — includes energy usage, EV adoption, solar generation, energy efficiency programs, and grid infrastructure datasets. List all available datasets in the PG&E Data Portals catalog
query_by_date_range
Specify the dataset ID and start/end dates to retrieve records within that time period. Use this for time-series analysis across any dataset type. Dataset ID from search_datasets. Dates in YYYY-MM-DD format. This is useful for year-over-year comparisons and trend analysis. Query any PG&E dataset filtered by a specific date range
query_dataset
Optional filters can be passed as key-value pairs to narrow results (e.g., zip_code, year, region). Use this to retrieve actual data records from any dataset in the PG&E Data Portals. Dataset IDs are obtained from search_datasets or list_all_datasets. Query a specific PG&E dataset with optional filters
query_energy_efficiency
), and investment amounts. Use this to analyze program effectiveness and ROI of energy efficiency initiatives. Optional programType filters by program category. Year is YYYY format. Query PG&E energy efficiency program data
query_energy_usage
Returns electricity usage aggregated by customer segment (residential, commercial, industrial, agricultural). Use this to analyze energy consumption patterns in specific geographic areas over time. ZIP code format: 5-digit (e.g., "94102"). Dates in YYYY-MM-DD format. Query PG&E energy consumption data by ZIP code and date range
query_ev_adoption
Use this to analyze EV adoption trends, identify high-adoption areas, and correlate with charging infrastructure. ZIP code is 5-digit format. Year is YYYY format (e.g., "2024"). Query electric vehicle adoption data by ZIP code and year
query_grid_infrastructure
Use this to understand grid capacity, identify areas needing upgrades, or analyze reliability metrics. Region filters by geographic area. dataType can filter by specific infrastructure type. Query PG&E grid infrastructure and distribution data
query_solar_generation
Use this to analyze solar adoption and production trends. Region can be a county name or service area identifier. Year is YYYY format. Query solar energy generation data by region and year
search_datasets
Use this to discover available datasets before querying specific data. Returns dataset names, descriptions, IDs, and metadata. Optional query parameter filters results by keyword. Search the PG&E Data Portals catalog for energy datasets
Example Prompts for PG&E Data Portals in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with PG&E Data Portals immediately.
"List all available PG&E datasets."
"Show me electricity usage for ZIP code 94102."
"Show EV adoption trends by ZIP code for 2024."
Troubleshooting PG&E Data Portals MCP Server with LlamaIndex
Common issues when connecting PG&E Data Portals to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPG&E Data Portals + LlamaIndex FAQ
Common questions about integrating PG&E Data Portals 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 Data Portals 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 Data Portals to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.