How to Use the Climatiq Carbon Calculations MCP in LlamaIndex

Q: How do I combine Climatiq Carbon Calculations with LlamaIndex vector search?

Use searchemissionfactors to pull active factors, then index them as nodes in your vector store. Your agent queries this local index before calling the live API.

Q: How do I run batch Climatiq Carbon Calculations within LlamaIndex?

Yes, pass your parsed document data to batchestimateemissions to run up to 100 calculations. The output is structured JSON that you can index immediately.

Q: What is the fastest way to register the Climatiq Carbon Calculations MCP Server in LlamaIndex?

Install llama-index-tools-mcp and instantiate BasicMCPClient with the Vinkius URL. Convert it to tools using McpToolSpec and pass them to your FunctionAgent.

Q: How does the agent know which units are valid for a calculation?

The agent executes listunits to retrieve allowed measurement units. It then maps the units from your documents to matching API values.

Index Climatiq Carbon Calculations results directly into LlamaIndex vector stores for RAG-driven sustainability insights.

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Connect Climatiq Carbon Calculations MCP to LlamaIndex

Create your Vinkius account to connect Climatiq Carbon Calculations 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 Climatiq Carbon Calculations with LlamaIndex

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Index Climatiq Carbon Calculations data in LlamaIndex

The `search_emission_factors` tool feeds live carbon factor data directly into your LlamaIndex document store. Your agent indexes these factors, making them instantly searchable for semantic queries using this MCP Server. Combining this with `list_sources` allows you to store the provenance of each emission factor. This lets your RAG pipeline cite the exact database source when explaining a carbon footprint calculation to a user.

Context-driven footprint estimation

The `estimate_emissions` tool computes the carbon impact of activities extracted from unstructured documents. Your LlamaIndex pipeline parses PDF invoices, extracts raw activity values, and passes them straight to the calculation engine. Running `batch_estimate_emissions` over this MCP connection lets your index process entire folders of utility bills at once. You convert raw text tables into structured carbon metrics stored directly in your vector database.

Filter calculations by region and sector

The `list_regions` tool gets supported regions to filter your index queries. Your LlamaIndex agent checks this tool to ensure it only queries active geographic areas during the calculation phase. Calling `list_sectors` and `list_years` allows the agent to build a structured metadata schema. This schema ensures your vector database filters carbon calculations accurately by year and industry sector.

Setup guide

Set up Climatiq Carbon Calculations 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 Climatiq Carbon Calculations 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 Climatiq Carbon Calculations tools.",
)
response = await agent.run("List recent Climatiq Carbon Calculations data")

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Common questions about Climatiq Carbon Calculations MCP in LlamaIndex

Use `search_emission_factors` to pull active factors, then index them as nodes in your vector store. Your agent queries this local index before calling the live API.

Yes, pass your parsed document data to `batch_estimate_emissions` to run up to 100 calculations. The output is structured JSON that you can index immediately.

Install `llama-index-tools-mcp` and instantiate `BasicMCPClient` with the Vinkius URL. Convert it to tools using `McpToolSpec` and pass them to your `FunctionAgent`.

The agent executes `list_units` to retrieve allowed measurement units. It then maps the units from your documents to matching API values.

All calculation payloads containing raw activity metrics pass through ephemeral, zero-trust sandboxes. Your sensitive operational data is never cached or written to persistent disk during the calculation.

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