Ember Climate MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Ember Climate as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Ember Climate. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Ember Climate?"
)
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 Ember Climate MCP Server
Connect your AI agents to Ember Climate's open electricity dataset and gain instant access to global energy intelligence covering over 200 countries and regions.
LlamaIndex agents combine Ember Climate tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through the 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
- Carbon Intensity Analysis — Track yearly and monthly carbon footprint (gCO2/kWh) of electricity grids worldwide
- Generation by Source — Break down electricity production by energy type: coal, gas, nuclear, wind, solar, hydro, and more
- Demand Trends — Analyze electricity consumption patterns in TWh with per-capita metrics across nations
- Power Sector Emissions — Monitor CO2 emissions from the power sector in megatonnes and percentage shares
- Renewable Capacity Tracking — Follow monthly wind and solar capacity installations in GW to measure clean energy deployment
- Multi-Country Comparison — Query multiple nations simultaneously using comma-separated country codes for comparative analysis
- Filter Discovery — Explore available entities, energy sources, and date ranges dynamically before making targeted queries
The Ember Climate MCP Server exposes 11 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 Ember Climate to LlamaIndex via MCP
Follow these steps to integrate the Ember Climate 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 11 tools from Ember Climate
Why Use LlamaIndex with the Ember Climate MCP Server
LlamaIndex provides unique advantages when paired with Ember Climate through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Ember Climate tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Ember Climate tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Ember Climate, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Ember Climate tools were called, what data was returned, and how it influenced the final answer
Ember Climate + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Ember Climate MCP Server delivers measurable value.
Hybrid search: combine Ember Climate real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Ember Climate 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 Ember Climate for fresh data
Analytical workflows: chain Ember Climate queries with LlamaIndex's data connectors to build multi-source analytical reports
Ember Climate MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Ember Climate to LlamaIndex via MCP:
get_api_options
Use dataset (e.g., "electricity-generation"), temporal_resolution (e.g., "monthly", "yearly"), and filter_name (e.g., "entity", "series", "entity_code", "date", "year"). This tool is useful for discovering valid country codes, energy source types, and available date ranges before making specific data queries. Get available filter options for Ember electricity datasets
get_carbon_intensity_monthly
Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY-MM (e.g., "2023-01", "2024-12"). This helps analyze seasonal patterns in grid carbon footprint and track monthly decarbonization progress. Get monthly carbon intensity of electricity generation for countries/regions
get_carbon_intensity_yearly
Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY (e.g., "2020", "2023"). Returns emissions intensity data showing how clean or polluting the electricity grid is over time. Get yearly carbon intensity of electricity generation for countries/regions
get_electricity_demand_monthly
Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY-MM (e.g., "2023-01", "2024-12"). Useful for analyzing seasonal demand patterns, peak consumption periods, and demand forecasting. Get monthly electricity demand data for countries/regions
get_electricity_demand_yearly
Use entity or entity_code to specify countries (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY (e.g., "2020", "2023"). Essential for understanding energy consumption trends and comparing per-capita usage across nations. Get yearly electricity demand data for countries/regions
get_electricity_generation_monthly
). Returns generation in TWh and percentage share of total generation for each source. Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY-MM (e.g., "2023-01", "2024-12"). Use series to filter by specific energy sources (e.g., "coal", "wind", "solar", "hydro", "nuclear", "gas"). Perfect for analyzing seasonal generation patterns, renewable intermittency, and monthly energy mix changes. Get monthly electricity generation by source for countries/regions
get_electricity_generation_yearly
). Returns generation in TWh and percentage share of total generation for each source. Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY (e.g., "2020", "2023"). Use series to filter by specific energy sources (e.g., "coal", "wind", "solar", "hydro", "nuclear", "gas"). Essential for analyzing energy transition, renewable adoption, and fossil fuel phase-out progress. Get yearly electricity generation by source for countries/regions
get_generation_multi_entity
g., "BRA,DE,US" for Brazil, Germany, and United States). Use start_date and end_date with format YYYY for yearly or YYYY-MM for monthly data. Use series to filter by energy source (e.g., "coal", "wind", "solar", "hydro", "nuclear", "gas"). This is highly efficient for comparative analysis across multiple nations without making separate API calls. Example: entity_code="BRA,DE,US,CHN,IND" to compare BRICS+ nations energy generation. Get electricity generation data for multiple countries simultaneously
get_installed_capacity_monthly
Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY-MM (e.g., "2023-01", "2024-12"). Use series to filter by capacity type (e.g., "wind", "solar"). Tracks renewable infrastructure deployment and capacity growth over time across different nations. Get monthly installed power capacity (wind and solar) for countries
get_power_sector_emissions_monthly
Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY-MM (e.g., "2023-01", "2024-12"). Use series parameter to filter by emission types (e.g., "co2"). Enables granular tracking of monthly emission trends and seasonal variations in power sector pollution. Get monthly power sector CO2 emissions for countries/regions
get_power_sector_emissions_yearly
Use entity or entity_code to filter by country (e.g., "Brazil", "DE", "US"). Use start_date and end_date with format YYYY (e.g., "2020", "2023"). Use series parameter to filter by emission types (e.g., "co2"). Critical for tracking national decarbonization progress and climate policy effectiveness. Get yearly power sector CO2 emissions for countries/regions
Example Prompts for Ember Climate in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Ember Climate immediately.
"What is the carbon intensity of Brazil's electricity grid in recent years?"
"Compare wind and solar generation between Germany, China, and the US for the last 3 years."
"Show me the monthly electricity demand in France during 2024."
Troubleshooting Ember Climate MCP Server with LlamaIndex
Common issues when connecting Ember Climate to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpEmber Climate + LlamaIndex FAQ
Common questions about integrating Ember Climate 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 Ember Climate 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 Ember Climate to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.