How to Use the Settlement Value Estimator MCP in Google ADK

Q: Can I filter which Settlement Value Estimator tools Google ADK uses?

You can use the toolnames filter in your toolset configuration. This restricts the agent to only the specific functions you want it to perform.

Bring litigation data from BigQuery into Google ADK agents.

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Connect Settlement Value Estimator MCP to Google ADK

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Setup Settlement Value Estimator with Google ADK

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Financial modeling with Google ADK

Use `calculate_emv` to process claims data directly from your Google Cloud environment. Your agent converts raw litigation metrics into expected value projections in seconds. This workflow is built for enterprise scale. You keep your agent logic centralized while the MCP handles the heavy lifting of financial math.

Negotiation strategy for Google ADK

Call `derive_negotiation_surfaces` to establish clear negotiation boundaries for your legal team. Your agent interprets these ranges to guide settlement discussions effectively. This tool simplifies the process of defining your best and worst alternatives. It ensures your agent always operates within your defined strategic framework.

Sensitivity testing in Google ADK

Trigger `perform_risk_sensitivity_analysis` to stress test your legal claims against varying win probabilities. Your agent analyzes the output to highlight potential financial exposure. This simulation provides the depth you need for high-stakes decision making. It allows your agent to account for risk tolerance before recommending a settlement path.

Setup guide

Set up Settlement Value Estimator MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Settlement Value Estimator tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Settlement Value Estimator_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Settlement Value Estimator tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Settlement Value Estimator MCP in Google ADK

You initialize the MCP using the McpToolset class and pass it to your LlmAgent. This allows your agent to call the tools directly within your existing cloud infrastructure.

Yes. The agent can ingest massive amounts of legal documentation and use these tools to extract precise settlement values without hitting context limits.

You can use the tool_names filter in your toolset configuration. This restricts the agent to only the specific functions you want it to perform.

The connection uses standard transport protocols that you manage. You maintain full control over the network path between your agent and the MCP.

The tool processes only the input parameters you send during a function call. It does not persist your sensitive case information, ensuring your data remains isolated within your Google Cloud project.

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