How to Use the Depreciation Calculator MCP in AutoGen

Q: What's the setup process for the Depreciation Calculator with AutoGen?

With the autogen-extmcp package, you use the mcpservertools function with the server's URL. This provides a list of tools that you can pass directly to your AssistantAgent when you create it.

Let your AutoGen agents debate and agree on the best asset depreciation strategy.

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Setup Depreciation Calculator with AutoGen

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Enable Multi-Agent Financial Debate

Equip your AutoGen agents with this calculator and watch them deliberate. One agent, acting as a conservative accountant, might argue for the `calculate_straight_line` method for its simplicity and predictability. Meanwhile, a more aggressive financial analyst agent could counter by running `calculate_declining_balance` to show the benefits of a faster write-off. The final decision comes from consensus, not a single command.

Simulate Financial Scenarios

AutoGen's strength is conversation, which is perfect for "what-if" analysis. You can task a team of agents to model the financial impact of purchasing a new piece of equipment. One agent can use the `calculate_sum_of_years_digits` tool to project the tax implications over five years, while another agent cross-references that with budget constraints. They work together, using the tool to build a shared understanding.

Build Consensus-Driven AutoGen MCP Solutions

This MCP provides the factual grounding for your agent conversations. When agents disagree on financial strategy, they can use the calculator as a neutral third party to get hard numbers. This stops debates from getting stuck on subjective opinions. The agents can propose a hypothesis, test it with a tool call, and then incorporate the result into the next round of discussion.

Setup guide

Set up Depreciation Calculator 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 Depreciation Calculator 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="Depreciation Calculator_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Depreciation Calculator data")
print(result.messages[-1].content)

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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="Depreciation Calculator_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Depreciation Calculator data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Depreciation Calculator. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Depreciation Calculator MCP in AutoGen

You assign the calculator's tools to one or more agents. During the conversation, an agent can decide to use a tool to get data, like calculating a depreciation schedule, and then post the result back into the chat for other agents to see.

Yes, that's a great use case. You can set up a "controller" agent and a "validator" agent. The controller proposes a financial plan using the Depreciation Calculator, and the validator re-runs the numbers to confirm their accuracy before the plan is approved.

With the `autogen-ext[mcp]` package, you use the `mcp_server_tools` function with the server's URL. This provides a list of tools that you can pass directly to your `AssistantAgent` when you create it.

Prompting formulas is unreliable and prone to errors. Using this MCP ensures the calculations are always correct and auditable. It provides a stable, predictable tool that your AutoGen agents can rely on.

Only the data for a specific calculation is sent. When an agent calls a tool, it sends only the required inputs (cost, salvage value, life). The MCP doesn't see the agent conversation or have any persistent knowledge of your assets.

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