Corrently Regional Green Index MCP Server for LlamaIndex 2 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Corrently Regional Green Index 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 Corrently Regional Green Index. "
"You have 2 tools available."
),
)
response = await agent.run(
"What tools are available in Corrently Regional Green Index?"
)
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 Corrently Regional Green Index MCP Server
Equip your AI agent with hyper-local energy intelligence through the Corrently MCP server. This integration provides real-time and forecast data for the Green Power Index (GSI), identifying when the electricity grid is cleanest in specific regions (primarily in Germany). Your agent can retrieve green index predictions by ZIP code and access current energy market prices. Whether you are automating smart home appliances, planning energy-intensive computing tasks, or researching regional grid sustainability, your agent acts as a dedicated regional energy consultant through natural conversation.
LlamaIndex agents combine Corrently Regional Green Index tool responses with indexed documents for comprehensive, grounded answers. Connect 2 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
- Regional Green Index — Get a clean energy forecast for any supported ZIP code.
- Market Pricing — Retrieve real-time electricity exchange prices and market data.
- Optimized Scheduling — Identify the best hours to consume electricity based on regional grid proactivity.
- Grid Transparency — Monitor the environmental performance of local energy infrastructure.
The Corrently Regional Green Index MCP Server exposes 2 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 Corrently Regional Green Index to LlamaIndex via MCP
Follow these steps to integrate the Corrently Regional Green Index 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 2 tools from Corrently Regional Green Index
Why Use LlamaIndex with the Corrently Regional Green Index MCP Server
LlamaIndex provides unique advantages when paired with Corrently Regional Green Index through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Corrently Regional Green Index tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Corrently Regional Green Index tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Corrently Regional Green Index, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Corrently Regional Green Index tools were called, what data was returned, and how it influenced the final answer
Corrently Regional Green Index + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Corrently Regional Green Index MCP Server delivers measurable value.
Hybrid search: combine Corrently Regional Green Index real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Corrently Regional Green Index 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 Corrently Regional Green Index for fresh data
Analytical workflows: chain Corrently Regional Green Index queries with LlamaIndex's data connectors to build multi-source analytical reports
Corrently Regional Green Index MCP Tools for LlamaIndex (2)
These 2 tools become available when you connect Corrently Regional Green Index to LlamaIndex via MCP:
get_energy_market_data
Get latest energy market prices
get_regional_green_index
Returns a forecast of when the grid is cleanest. Get green electricity index for a ZIP code
Example Prompts for Corrently Regional Green Index in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Corrently Regional Green Index immediately.
"What is the green energy index for ZIP code 10117 (Berlin)?"
"Check the green power forecast for Munich (ZIP 80331)."
"Show me the current energy market prices."
Troubleshooting Corrently Regional Green Index MCP Server with LlamaIndex
Common issues when connecting Corrently Regional Green Index to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCorrently Regional Green Index + LlamaIndex FAQ
Common questions about integrating Corrently Regional Green Index 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 Corrently Regional Green Index 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 Corrently Regional Green Index to LlamaIndex
Get your token, paste the configuration, and start using 2 tools in under 2 minutes. No API key management needed.