How to Use the EIA Coal & Mining — Solid Fuels Intelligence MCP in LlamaIndex

Q: What are some RAG examples for EIA Coal & Mining — Solid Fuels Intelligence and LlamaIndex?

You could build a RAG app that ingests daily getcoalquality reports. When a user asks "Which regions have the cleanest coal?", the app retrieves the relevant indexed reports and synthesizes a grounded answer.

Q: Can I use this to track changes in coal quality over time?

Absolutely. Schedule a daily or weekly call to getcoalquality for specific regions and index the results. Your LlamaIndex application can then visualize or report on trends in sulfur, ash, or heat content over months or years.

Turn live EIA coal data into a searchable knowledge base for your LlamaIndex RAG applications.

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Connect EIA Coal & Mining — Solid Fuels Intelligence MCP to LlamaIndex

Create your Vinkius account to connect EIA Coal & Mining — Solid Fuels Intelligence 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 EIA Coal & Mining — Solid Fuels Intelligence with LlamaIndex

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Index Real-Time Coal Prices

This server gives you the tools to build a historical market index. Set up a job that calls `get_coal_prices` daily and ingests the results into a LlamaIndex vector store. Now your agent can answer questions like, "How did subbituminous prices react to last quarter's production changes?" Your agent isn't just getting a live price; it's querying a knowledge base you built from the tool's output. This grounds its answers in historical data, preventing it from making things up. It can cite the exact date and price from past `get_coal_prices` calls.

Build a Queryable Mine Profile Index

Don't just get data, make it searchable. You can index the outputs of `get_mine_production` and `get_coal_quality` for every major mine. Your agent can then perform semantic searches over this knowledge base to find answers that require multiple data points. Ask your agent to "find all mines in Pennsylvania with heat content over 12,000 Btu/lb and sulfur below 1%." It will query your index—built from actual tool calls—and return a precise list. That's the power of combining this MCP Server with LlamaIndex.

Ground Trade Analysis in Indexed Data

Your agent's analysis is only as good as its data. Use LlamaIndex to build a private knowledge base from the `get_coal_trade` and `get_coal_reserves` tools. When you ask about supply chain risks, the agent retrieves historical trade disruptions from your index to inform its answer. This means the agent's conclusions about future risk are grounded in past events recorded by the tools. It can compare current import prices from `get_coal_trade` against a 12-month indexed average to spot anomalies, providing answers backed by evidence.

Setup guide

Set up EIA Coal & Mining — Solid Fuels Intelligence 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 EIA Coal & Mining — Solid Fuels Intelligence 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 EIA Coal & Mining — Solid Fuels Intelligence tools.",
)
response = await agent.run("List recent EIA Coal & Mining — Solid Fuels Intelligence data")

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Common questions about EIA Coal & Mining — Solid Fuels Intelligence MCP in LlamaIndex

You use the LlamaIndex MCP tool specification to wrap the client. This converts all the server's tools, like `get_mine_production`, into a format that a LlamaIndex agent can use directly in a query engine or RAG pipeline.

Yes, that's the main idea behind using LlamaIndex. You run the tools and ingest their outputs into a vector index. Your agent can then query that index to get answers based on historical `get_coal_prices` or `get_coal_production` data.

You could build a RAG app that ingests daily `get_coal_quality` reports. When a user asks "Which regions have the cleanest coal?", the app retrieves the relevant indexed reports and synthesizes a grounded answer.

Absolutely. Schedule a daily or weekly call to `get_coal_quality` for specific regions and index the results. Your LlamaIndex application can then visualize or report on trends in sulfur, ash, or heat content over months or years.

You control where the data goes. The tool outputs, like mine production stats or trade volumes, are sent to the vector store you configure in your LlamaIndex application. The Vinkius platform just handles the secure, ephemeral transport of the data to your agent.

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