Ember Climate MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Ember Climate through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"ember-climate": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Ember Climate, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Ember Climate through native MCP adapters. Connect 11 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Ember Climate MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from Ember Climate via MCP
Why Use LangChain with the Ember Climate MCP Server
LangChain provides unique advantages when paired with Ember Climate through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Ember Climate MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Ember Climate queries for multi-turn workflows
Ember Climate + LangChain Use Cases
Practical scenarios where LangChain combined with the Ember Climate MCP Server delivers measurable value.
RAG with live data: combine Ember Climate tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Ember Climate, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Ember Climate tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Ember Climate tool call, measure latency, and optimize your agent's performance
Ember Climate MCP Tools for LangChain (11)
These 11 tools become available when you connect Ember Climate to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Ember Climate to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersEmber Climate + LangChain FAQ
Common questions about integrating Ember Climate MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.