How to Use the Ember Climate MCP in LlamaIndex

Q: Can I index Ember Climate outputs for semantic search in LlamaIndex?

Yes, you can direct tool outputs from getcarbonintensityyearly into your vector store. This indexes the grid emissions data, making it searchable for future RAG queries.

Q: Can I filter which tools are visible to my LlamaIndex agent?

Yes, LlamaIndex allows you to pass an allowedtools list when initializing your tool spec. You can expose only getelectricitygenerationmonthly if you want to restrict your agent to generation data.

Index live global grid data from Ember Climate into LlamaIndex vector stores for hallucination-free energy RAG.

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Connect Ember Climate MCP to LlamaIndex

Create your Vinkius account to connect Ember Climate 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 Ember Climate with LlamaIndex

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Index live Ember Climate data into vector stores

LlamaIndex excels at turning raw Ember Climate API responses from `get_electricity_generation_yearly` into searchable, indexed knowledge. When you call `get_electricity_generation_yearly` in LlamaIndex, the structured Ember Climate output is converted into document nodes and stored in your vector database. This means your LlamaIndex agent can query historical solar or wind trends using semantic search instead of raw Ember Climate SQL queries. Grounding your LlamaIndex context in actual data returned by the Ember Climate `get_installed_capacity_monthly` tool eliminates common hallucination issues.

Query historical grid trends with RAG

Combining static documents with live Ember Climate APIs like `get_power_sector_emissions_monthly` gives your LlamaIndex application a complete view of the market. You can write a LlamaIndex query engine that checks your internal PDF reports alongside live data from Ember Climate's `get_power_sector_emissions_monthly`. The resulting LlamaIndex context integrates policy documents with actual, measured Ember Climate emissions. To make this work, the LlamaIndex `BasicMCPClient` connects directly to your Ember Climate Vinkius endpoint, letting you convert the Ember Climate tools into indexable resources that your LlamaIndex query engine can scan.

Connect this MCP Server to LlamaIndex agents

LlamaIndex agents use Ember Climate tools like `get_electricity_demand_monthly` to fetch precise data points on demand. If a user asks about winter heating demands, the LlamaIndex agent calls `get_electricity_demand_monthly` to fetch the exact Ember Climate numbers. The LlamaIndex agent then index-searches past winter trends to write a detailed comparison using Ember Climate metrics. Setting up this LlamaIndex integration with Ember Climate is straightforward, requiring you to run pip install llama-index-tools-mcp to register all eleven Ember Climate energy tools with your LlamaIndex agent.

Setup guide

Set up Ember Climate 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 Ember Climate 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 Ember Climate tools.",
)
response = await agent.run("List recent Ember Climate 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 Ember Climate. 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 Ember Climate MCP in LlamaIndex

Run pip install llama-index-tools-mcp to install the required client package. Use the BasicMCPClient to connect to your Vinkius URL, then convert the endpoints to tools using McpToolSpec.

Yes, you can direct tool outputs from `get_carbon_intensity_yearly` into your vector store. This indexes the grid emissions data, making it searchable for future RAG queries.

Instead of guessing energy metrics, your agent pulls verified data directly from `get_electricity_demand_yearly`. This ensures your LlamaIndex application answers queries using actual, up-to-date global demand statistics.

Yes, LlamaIndex allows you to pass an allowed_tools list when initializing your tool spec. You can expose only `get_electricity_generation_monthly` if you want to restrict your agent to generation data.

Your data requests are executed in a zero-trust environment that deletes all transaction logs instantly. The demand and capacity metrics fetched via `get_electricity_demand_yearly` flow directly to your vector store without leaving any footprint on our servers.

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