How to Use the Corrently Energy MCP in LlamaIndex

Q: How do I index German zip code forecasts using Corrently Energy and LlamaIndex?

You use the getgsiprediction tool to pull regional green energy data, then convert the JSON output into Document nodes within your LlamaIndex vector store. This makes the local GrünstromIndex searchable for your agent.

Q: Can I use Corrently Energy tools as query engines in LlamaIndex?

Yes, you can wrap tools like getbesthour inside an McpToolSpec to let your LlamaIndex FunctionAgent query the German grid directly. The agent decides whether to pull from its vector index or fetch live grid data.

Q: How does the Corrently Energy MCP Server prevent hallucination in LlamaIndex?

By exposing real-time tools like getco2meter, the server feeds verified emissions data directly into your agent's context window. This ensures any carbon calculations are grounded in actual German grid metrics.

Q: How do I track solar panel output forecasts in my index?

You call getsolarprediction with your panel's kWp capacity and zip code, then index the resulting forecast. Your LlamaIndex agent uses this data to answer questions about expected PV output over the next 24 hours.

Q: Is my Stromkonto account data safe when indexing with LlamaIndex?

Yes, when you fetch your balance via getstromkontobalance, the sensitive account ID and financial credits are processed locally inside the secure MCP sandbox. This sensitive German energy data is never uploaded to public LLM providers.

Index live German grid data into LlamaIndex using this MCP Server for grounded energy RAG.

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Connect Corrently Energy MCP to LlamaIndex

Create your Vinkius account to connect Corrently Energy 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 Corrently Energy with LlamaIndex

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Index live German energy data into LlamaIndex

This MCP Server exposes tools like `get_gsi_prediction` to let your LlamaIndex agent query live German green energy forecasts and store the results in your vector index. This prevents your agent from hallucinating energy prices or grid carbon levels. By indexing the output of `get_co2_prediction`, you build a searchable history of grid emissions over time. Your agent queries this local index to answer complex questions about regional carbon intensity without hitting the live API repeatedly.

Ground hybrid vehicle decisions in historical pricing

The `get_phev_charge_or_fuel` tool provides raw decision metrics that your LlamaIndex pipeline indexes for semantic search. Your agent compares past charging recommendations against current grid data to find patterns in regional fuel-to-charge transitions. Combining this with `get_market_data` lets you build a query engine that understands when German electricity rates are historically lowest. The agent retrieves these indexed pricing nodes to justify its charging recommendations to the user.

Analyze grid dispatch and merit order lists

The `get_dispatch` tool feeds live wind and solar feed-in data directly into your LlamaIndex knowledge base. Your agent analyzes this renewable contribution alongside `get_merit_order_list` to map out which power plants are currently active. This structured data is indexed as document nodes, allowing your agent to perform semantic retrieval over complex German energy market structures. You get a fully grounded analysis of grid composition based on actual market data rather than static training sets.

Setup guide

Set up Corrently Energy 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 Corrently Energy 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 Corrently Energy tools.",
)
response = await agent.run("List recent Corrently Energy data")

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Common questions about Corrently Energy MCP in LlamaIndex

You use the `get_gsi_prediction` tool to pull regional green energy data, then convert the JSON output into Document nodes within your LlamaIndex vector store. This makes the local GrünstromIndex searchable for your agent.

Yes, you can wrap tools like `get_best_hour` inside an McpToolSpec to let your LlamaIndex FunctionAgent query the German grid directly. The agent decides whether to pull from its vector index or fetch live grid data.

By exposing real-time tools like `get_co2_meter`, the server feeds verified emissions data directly into your agent's context window. This ensures any carbon calculations are grounded in actual German grid metrics.

You call `get_solar_prediction` with your panel's kWp capacity and zip code, then index the resulting forecast. Your LlamaIndex agent uses this data to answer questions about expected PV output over the next 24 hours.

Yes, when you fetch your balance via `get_stromkonto_balance`, the sensitive account ID and financial credits are processed locally inside the secure MCP sandbox. This sensitive German energy data is never uploaded to public LLM providers.

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