How to Use the ISO New England MCP in LlamaIndex

Index live ISO New England market data into your LlamaIndex knowledge base for grounded, document-backed analysis.

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Connect ISO New England MCP to LlamaIndex

Create your Vinkius account to connect ISO New England to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ISO New England with LlamaIndex

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Perplexity

Ground your RAG applications with LlamaIndex

Don't settle for stale documents. Pull fresh data from `get_fuel_mix` and index it directly into your vector store. Your queries now return answers based on current grid state. LlamaIndex treats the MCP output as a primary knowledge source.

Search past energy trends

Store results from `get_hourly_load` in your index over time. You create a searchable history of demand patterns. This lets you query your own database of grid performance. Stop hallucinating about past spikes and look at the actual logged values.

Combine docs and live market prices

Mix static policy PDFs with real-time `get_lmp` data. Your agent retrieves the policy and the current price in one go. This gives your LlamaIndex setup context that static files lack. You get a complete picture of the market right now.

Setup guide

Set up ISO New England MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all ISO New England MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to ISO New England tools.",
)
response = await agent.run("List recent ISO New England data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ISO New England. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ISO New England MCP in LlamaIndex

It can. Use the MCP tool spec to pull live values. These results become part of your index for immediate retrieval.

You define the storage. Once the MCP tool fetches the data, your index persists it according to your vector store settings.

They will. The server responds in milliseconds. Indexing happens as soon as the agent resolves the tool call.

They are exact. We return the raw numeric data from the grid operator for your index to process without loss.

Your data is handled by your local runtime. We enforce strict transport security between the server and your agent.

Use it with your favorite AI tools

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