How to Use the Document360 MCP in LangChain

Q: Can I use Document360 with LangChain for automated search?

Yes. You just pass the search tool into your agent definition. It handles the query and returns the matching snippets directly to the chain.

Q: How does this MCP server handle Document360 project versions?

The server provides a listprojects tool that exposes all available versions. Your agent queries this list first to ensure it's pulling data from the correct project environment.

Q: Does LangChain support real-time Document360 analytics?

It does. By integrating the analytics tool, your agent retrieves live performance data. You can then use that data to trigger downstream tasks or alerts.

Q: How do I list team members in Document360 using LangChain?

Simply call the listteam tool. It provides a list of accounts, which you can use for routing documentation tasks to the right person.

Connect your LangChain agents directly to your documentation and analytics using this Document360 MCP server.

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Connect Document360 MCP to LangChain

Create your Vinkius account to connect Document360 to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain documentation tasks in LangChain

Feed your agent project data so it understands your knowledge base structure immediately. Your chain uses `list_projects` and `list_categories` to map out the documentation hierarchy before executing any logic. It removes the need for manual navigation. Your agent traverses the tree, identifies the right category, and pulls content without you lifting a finger.

Automate content retrieval with MCP

Use `get_article` and `list_articles` to inject specific documentation into your reasoning loops. Your agent grabs exactly what it needs to answer user questions or generate technical summaries. This keeps your context window clean. By calling these tools, the agent pulls only the relevant text, ignoring the rest of the noise in your knowledge base.

Monitor knowledge impact via analytics

Pass `analytics` data directly into your LangChain workflow to track how your docs perform. You can build a chain that identifies high-traffic articles and flags them for updates. It turns raw metrics into actionable tasks. You get a clear picture of what readers are actually looking at, allowing your agent to prioritize documentation maintenance.

Setup guide

Set up Document360 MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Document360 tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "document360-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Document360 transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Document360 MCP in LangChain

Yes. You just pass the `search` tool into your agent definition. It handles the query and returns the matching snippets directly to the chain.

The server provides a `list_projects` tool that exposes all available versions. Your agent queries this list first to ensure it's pulling data from the correct project environment.

It does. By integrating the `analytics` tool, your agent retrieves live performance data. You can then use that data to trigger downstream tasks or alerts.

The server operates within a V8 sandbox. It only touches the article titles, content, and metadata you explicitly request via your client.

Simply call the `list_team` tool. It provides a list of accounts, which you can use for routing documentation tasks to the right person.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python