How to Use the Document360 MCP in OpenAI Agents SDK

Connect Document360 to your OpenAI Agents SDK projects to automate documentation retrieval and analytics.

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Connect Document360 MCP to OpenAI Agents SDK

Create your Vinkius account to connect Document360 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 Document360 with OpenAI Agents SDK

Ask AI about this MCP

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Claude

Perplexity

Manage documentation versions and structure

Your agent uses `list_projects` to identify available documentation versions before navigating the hierarchy with `list_categories`. This gives the agent a map of your knowledge base before it touches a single file. Once the structure is clear, the agent calls `list_articles` to scope out specific content. It's a clean way to handle multi-version docs without manual indexing.

Retrieve and analyze Document360 content

The agent pulls raw content using `get_article` whenever it needs to answer user questions. It treats your documentation as a live data source rather than a static dump. Use `analytics` to monitor how readers interact with your docs. Your agent can report back on performance metrics, flagging articles that need updates based on real traffic data.

Search and team visibility for OpenAI Agents SDK

Run `search` to find relevant information across your entire knowledge base in seconds. The agent parses the results to provide accurate answers without needing to browse every category manually. Use `list_team` to verify who has access to your Document360 environment. This keeps your agent aware of the people behind the content it manages.

Setup guide

Set up Document360 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 Document360 tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Document360 tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Document360 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="Document360 Agent",
            instructions="You have access to Document360 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 Document360. 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 Document360 MCP in OpenAI Agents SDK

The server executes a direct query against your knowledge base. It returns the most relevant matches immediately, allowing the agent to synthesize answers from your actual documentation.

No. This MCP Server is currently built for discovery and retrieval. It allows your agent to read and analyze data, not modify your existing records.

Yes. The connection relies on a single endpoint token managed by Vinkius. Your data is never stored by the server, and all communication is ephemeral.

The server passes the error code directly to your agent. You can then handle the exception within your Python code to log the failure or try a different query.

You can filter the tool list when initializing the server connection. This restricts the agent to only the specific operations you want it to perform.

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