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

Q: How can my AutoGen agents use tools from the EIA Coal & Mining — Solid Fuels Intelligence server?

You use the autogen-ext library to register the MCP Server tools. The tool adapter automatically handles converting the schemas. You can then assign specific tools, like getcoalprices, to different agents in your group.

Equip your AutoGen agents with EIA coal data to debate energy strategy, risk, and market moves.

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

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

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Perplexity

Pit a Trader Against a Risk Analyst

This MCP Server lets you build agent teams that argue from data. Give one agent the `get_coal_prices` tool and instruct it to find buying opportunities. Give a second agent the `get_coal_production` tool and task it with finding supply risks. The agents will then debate each other. The trader might see a low price, but the analyst will counter with data showing declining production in that region. Through conversation, they arrive at a more balanced conclusion than a single agent ever could.

Negotiate Environmental Compliance

Use AutoGen's multi-agent conversations to model complex decisions. An 'Operations' agent can use `get_mine_production` to find a high-output coal source. A 'Compliance' agent, armed with `get_coal_quality`, can then flag that source for its high sulfur content. This forces a negotiation. The agents must discuss the trade-offs between production volume and environmental impact, using hard data from the tools to back up their arguments. The final plan is a consensus, not a single command.

Pressure-Test Plans with Your AutoGen MCP Server

Have your agents challenge each other's assumptions. An 'Investment' agent might propose a strategy based on domestic supply, using data from `get_coal_reserves`. You can then introduce a 'Skeptic' agent whose job is to find fault with the plan. This skeptic agent can use the `get_coal_trade` tool to find cheaper international sources, forcing the first agent to defend its domestic-first strategy. The result is a plan that has survived internal scrutiny, all orchestrated within your AutoGen environment.

Setup guide

Set up EIA Coal & Mining — Solid Fuels Intelligence MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes EIA Coal & Mining — Solid Fuels Intelligence tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="EIA Coal & Mining — Solid Fuels Intelligence_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent EIA Coal & Mining — Solid Fuels Intelligence data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="EIA Coal & Mining — Solid Fuels Intelligence_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent EIA Coal & Mining — Solid Fuels Intelligence data")
    print(result.messages[-1].content)

Get your connection token →

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

You use the `autogen-ext` library to register the MCP Server tools. The tool adapter automatically handles converting the schemas. You can then assign specific tools, like `get_coal_prices`, to different agents in your group.

Yes, and that's the point. You can create specialists. Give your 'Market Analyst' agent access to `get_coal_prices` and `get_coal_trade`, while your 'Geologist' agent gets `get_coal_reserves` and `get_coal_quality`.

A classic setup is a triad: a 'Proposer' with access to production and price tools, a 'Critic' with access to quality and trade tools, and a 'Moderator' that guides the conversation to a final decision based on the data presented.

It's a perfect fit. You can create agents representing different stakeholders—a utility, a regulator, a mining company—and give them the specific tools they'd use. Their resulting debate will be grounded in the same data real stakeholders use.

Data is shared through the conversation history. When one agent calls `get_mine_production` and posts the result, that data becomes part of the context for all other agents. The Vinkius MCP Server connection itself is isolated and ephemeral for each tool call.

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