How to Use the UniCourt MCP in LlamaIndex

Q: How does UniCourt and LlamaIndex handle case searches?

You run searchcases using the MCP Server. The resulting list of cases gets indexed into your vector store, making every record semantically searchable later on.

Q: Can I track updates with UniCourt and LlamaIndex?

Yes. When you run requestcaseupdate, the status check using getcaseupdatestatus provides data that is immediately indexed. You can query historical update cycles.

Q: How do I manage case documents using UniCourt and LlamaIndex?

You can use ordercasedocument to request materials. The resulting file URL is then passed back through getdocumentordercallback, ensuring the document record is fully captured in your index.

Q: What data privacy features exist with UniCourt and LlamaIndex?

The server touches case records, including party names (searchnormparty) and specific legal documents. By indexing this raw API data, you control exactly what knowledge is stored in your vector store.

Index UniCourt legal records into a knowledge base with LlamaIndex.

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Connect UniCourt MCP to LlamaIndex

Create your Vinkius account to connect UniCourt to LlamaIndex 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 LlamaIndex

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Build RAG applications from case data using the MCP Server.

The tool output becomes part of your searchable index. For example, after calling `get_case` to retrieve specific records, LlamaIndex indexes that raw JSON data. Later queries can then be grounded in actual API details, not just general knowledge.

Search and synthesize historical UniCourt case records with LlamaIndex.

You can run `search_cases` to find current litigation, but the results are indexed. This means your agent can query past sessions or configurations against that data, retrieving answers grounded in concrete court record API outputs.

Automate data exports and track status with UniCourt/LlamaIndex.

When you request a large export using `request_case_export`, the file URL is retrieved via `get_case_export_callback`. LlamaIndex indexes these callback results, allowing your system to query historical success or failure reports on data exports.

Setup guide

Set up UniCourt MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all UniCourt MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to UniCourt tools.",
)
response = await agent.run("List recent UniCourt data")

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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 LlamaIndex

You run `search_cases` using the MCP Server. The resulting list of cases gets indexed into your vector store, making every record semantically searchable later on.

Yes. When you run `request_case_update`, the status check using `get_case_update_status` provides data that is immediately indexed. You can query historical update cycles.

UniCourt offers detailed normalized data for parties, judges, attorneys, and law firms (`get_norm_party`, `get_norm_judge`). These structured datasets are ideal for building a robust knowledge index.

You can use `order_case_document` to request materials. The resulting file URL is then passed back through `get_document_order_callback`, ensuring the document record is fully captured in your index.

The server touches case records, including party names (`search_norm_party`) and specific legal documents. By indexing this raw API data, you control exactly what knowledge is stored in your vector store.

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