How to Use the Lexzur MCP in LlamaIndex

Q: How do I index Lexzur documents using LlamaIndex?

Use BasicMCPClient to connect to Lexzur, then wrap it in McpToolSpec. Your LlamaIndex agent can then pull metadata from listdocuments to feed your vector store.

Q: Can LlamaIndex search through Lexzur company directories?

Yes. By indexing the output of the Lexzur listcompanies tool, your LlamaIndex RAG pipeline can instantly resolve client names and match them to active cases.

Q: Can I load Lexzur resources directly into my LlamaIndex index?

Yes. Set includeresources=True in your tool spec to let LlamaIndex read raw data streams directly from the Lexzur MCP Server.

Q: How does LlamaIndex protect my Lexzur time logs and billing data?

Your Lexzur billing hours retrieved from listtimelogs are processed locally within your LlamaIndex sandbox. No financial data is cached or stored outside your designated vector database.

Index live Lexzur legal data into LlamaIndex vector stores to run precise RAG queries without hallucinations.

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Connect Lexzur MCP to LlamaIndex

Create your Vinkius account to connect Lexzur 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 Lexzur with LlamaIndex

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Index Lexzur legal matters for semantic search

This MCP Server enables LlamaIndex to ingest active Lexzur case details from `list_matters` and `get_matter` into vector embeddings. Your LlamaIndex search agent queries this live vector store to find historical precedents or case details in Lexzur.

Query live litigation records with LlamaIndex RAG

This MCP Server exposes court schedules and files via `list_litigation` and `list_documents` directly to your LlamaIndex RAG application. The LlamaIndex retriever queries these live Lexzur documents to answer specific questions about upcoming trial dates.

Filter allowed tools to control agent access

This MCP Server integrates with LlamaIndex's tool filtering to restrict access to sensitive Lexzur endpoints like `list_invoices` while keeping `list_contacts` and `list_tasks` open. This configuration ensures your LlamaIndex query engine only retrieves non-sensitive Lexzur tasks without exposing partner billing details.

Setup guide

Set up Lexzur 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 Lexzur 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 Lexzur tools.",
)
response = await agent.run("List recent Lexzur 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 Lexzur. 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 Lexzur MCP in LlamaIndex

Use `BasicMCPClient` to connect to Lexzur, then wrap it in `McpToolSpec`. Your LlamaIndex agent can then pull metadata from `list_documents` to feed your vector store.

Yes. By indexing the output of the Lexzur `list_companies` tool, your LlamaIndex RAG pipeline can instantly resolve client names and match them to active cases.

The LlamaIndex framework forces the agent to ground its answers in the live JSON payloads retrieved from Lexzur endpoints like `list_tasks` or `get_matter`.

Yes. Set `include_resources=True` in your tool spec to let LlamaIndex read raw data streams directly from the Lexzur MCP Server.

Your Lexzur billing hours retrieved from `list_time_logs` are processed locally within your LlamaIndex sandbox. No financial data is cached or stored outside your designated vector database.

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