How to Use the UniCourt MCP in Google ADK

Q: How can I search for court cases with UniCourt using Google ADK?

Use the searchcases tool. It returns details on matching records, which your agent then processes and loads into BigQuery tables.

Q: Can the Google ADK help me track updates for UniCourt cases?

Yes, you can use trackcase. This tool monitors case status changes over time. It ensures your data in Vertex AI reflects the most current legal standing.

Q: Do I need to import cases before using UniCourt with Google ADK?

If the data isn't in your system, you can use importcase for high-priority imports or call importpacercase if it comes directly from PACER.

Q: How do I find information on normalized judges with UniCourt using Google ADK?

Use the searchnormjudge tool. It provides analytics and details about specific judges, which you can then join with other data sets in BigQuery.

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Connect UniCourt MCP to Google ADK

Create your Vinkius account to connect UniCourt to Google ADK 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 UniCourt with Google ADK

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Analyze Legal Sources using Google ADK

Need to know who's involved? Use `search_norm_party` or `get_norm_law_firm`. These tools give you analytics and details on parties and law firms, making sure your BigQuery models are fed accurate source data. Google ADK lets your agent ingest this clean data directly into your existing Google Cloud infrastructure for further processing.

Requesting Case Data Exports with MCP Server

The `request_case_export` tool handles massive data pulls, giving you a file URL when the ZIP is ready. This lets you pull entire case records into your BigQuery warehouse. Similarly, if you only need documents, use `order_case_document` to get specific files and then track them with `get_document_order_callback`.

Initial Case Retrieval for Google ADK

To start, run the `get_case` tool. It fetches all details for a specific case number you provide. This is your starting point before building complex pipelines. If you need to find a record but don't have the ID, use `search_cases`. The agent finds matching cases and feeds the metadata directly into Gemini models.

Setup guide

Set up UniCourt 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 UniCourt 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="UniCourt_agent",
    model="gemini-2.0-flash",
    instruction="You have access to UniCourt tools via MCP.",
    tools=mcp_tools,
)

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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 UniCourt. 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 UniCourt MCP in Google ADK

Use the `search_cases` tool. It returns details on matching records, which your agent then processes and loads into BigQuery tables.

Yes, you can use `track_case`. This tool monitors case status changes over time. It ensures your data in Vertex AI reflects the most current legal standing.

This server touches sensitive court records, specifically party names, case IDs, and document details. Access requires proper authentication managed by tools like `get_pacer_credential`.

If the data isn't in your system, you can use `import_case` for high-priority imports or call `import_pacer_case` if it comes directly from PACER.

Use the `search_norm_judge` tool. It provides analytics and details about specific judges, which you can then join with other data sets in BigQuery.

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