How to Use the Attorney Fees Calculator MCP in AutoGen

Equip your AutoGen agents to debate legal billing strategies. Let them argue over fee structures and reach a consensus on the best approach.

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Setup Attorney Fees Calculator with AutoGen

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Give Each Agent a Financial Perspective

This MCP provides the financial tools for a proper multi-agent debate. You can have one agent, the 'Cost Analyst,' use `hourly` to track burn rate, while another agent, the 'Team Lead,' uses `team` to project total manpower costs for a case. In an AutoGen conversation, these agents can then present their findings. The Cost Analyst might argue for a smaller team based on the `hourly` data, while the Team Lead defends their resource allocation. It's how you simulate real-world budget discussions.

Let Agents Argue Flat Fee vs. Contingency

Different agents can champion different billing models. A 'Risk-Averse' agent could favor the predictability of a `flat_fee` for standard services. It can pull the numbers and argue for a fixed, known cost. Meanwhile, a 'Growth' agent could use the `contingency` tool to model the potential upside of a successful case, arguing that the higher potential reward justifies the risk. AutoGen lets these two perspectives clash, using data from this MCP to back up their arguments.

Converge on an Optimal Strategy with the MCP Server

When agents disagree, you need a tie-breaker. The `fee` tool provides objective data for comparison. An 'Auditor' agent can be tasked with running a final analysis to settle a debate between other agents. This agent calls `fee` to find the exact break-even point between hourly and flat-fee models. It can then present this number to the group, forcing a data-driven conclusion. The Attorney Fees Calculator MCP provides the facts that help your agent group move from debate to decision.

Setup guide

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

result = await agent.run("List recent Attorney Fees 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="Attorney Fees Calculator_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Attorney Fees 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 Attorney Fees 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 Attorney Fees Calculator MCP in AutoGen

You pass the tools from this MCP to your AssistantAgent's constructor. Then, in a group chat, an agent can decide to call a tool like `contingency` to support its argument in a debate with other agents.

Absolutely. You can design a 'Cost' agent that only has access to `hourly` and a 'Strategy' agent that uses `flat_fee`. This is a core concept in AutoGen, where agents have specialized roles and tools.

It helps uncover the best strategy. One agent might push for a low-risk flat fee, while another sees a high reward in a contingency plan. The debate, fueled by data from the tools, helps find the optimal path that balances risk and reward.

Not at all. The MCP is isolated from your AutoGen framework. It only receives the inputs for a specific function call (e.g., hours and rate for the `hourly` tool) and has zero visibility into your agent chats.

Your connection to the service is always encrypted. The MCP itself is designed to be stateless, so sensitive billing details like fee structures or client recovery amounts are processed and immediately discarded. Nothing from your agent conversations is ever stored.

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