How to Use the CloudLex Legal MCP in OpenAI Agents SDK

Q: Can I use CloudLex Legal to search for documents inside OpenAI Agents SDK?

Yes, you use the listcloudlexdocuments tool to pull file lists for any active case. The agent then processes these names to locate specific records.

Build production legal agents with the OpenAI Agents SDK and CloudLex Legal to manage case files and client records directly.

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Connect CloudLex Legal MCP to OpenAI Agents SDK

Create your Vinkius account to connect CloudLex Legal to OpenAI Agents SDK 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 CloudLex Legal with OpenAI Agents SDK

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Automate case and client creation

Your OpenAI agent generates records inside your firm's database without manual entry. Use `create_cloudlex_case` to spin up new matters or `create_cloudlex_client` to add contacts instantly. Python scripts handle the heavy lifting while you maintain strict logic. The agent validates input before executing these calls in your production environment.

Query legal records via OpenAI Agents SDK

Stop digging through dashboards to find specific files. The `list_cloudlex_cases` and `list_cloudlex_clients` tools pull exact data into your agent's context window. Your code filters these results by status or practice area. You get the facts you need to advise clients without switching tabs.

Trace legal data interactions

Every action your agent takes stays visible in your logs. When you use `get_cloudlex_case` or `list_cloudlex_tasks`, the OpenAI dashboard records the full trace. This gives you proof of what your agent did. You keep full control over your firm's sensitive information at all times.

Setup guide

Set up CloudLex Legal MCP in OpenAI Agents SDK

Prerequisites

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

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all CloudLex Legal tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives CloudLex Legal tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate CloudLex Legal tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="CloudLex Legal Agent",
            instructions="You have access to CloudLex Legal tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 CloudLex Legal. 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 CloudLex Legal MCP in OpenAI Agents SDK

You plug the server endpoint directly into your agent definition. The SDK automatically detects the available tools and maps them to your local function calls.

Yes, you use the `list_cloudlex_documents` tool to pull file lists for any active case. The agent then processes these names to locate specific records.

Your data stays behind strict authentication layers provided by the Vinkius platform. The connection remains encrypted and isolated from other users.

The server returns an error message that your Python code catches immediately. You can then trigger a retry or flag the issue for human review.

We treat client names and incident dates as sensitive strings that never persist in model training. Only your agent sees this data during the execution cycle.

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