How to Use the Directus MCP in LangChain

Q: How do I pass Directus schema data into a LangChain chain using this MCP Server?

Use MultiServerMCPClient to connect to this server and pull tools like listschemacollections. Pass these tools into createagent so your LangChain agent can query the active PostgreSQL tables dynamically.

Q: How do I retrieve a single database row using this server?

The agent calls getcollectiondetails to verify the table structures before retrieving a row with getsingleitem.

Run multi-step LangChain chains that audit schemas and update database records directly through this Directus MCP Server.

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Connect Directus MCP to LangChain

Create your Vinkius account to connect Directus 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 Directus with LangChain

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Chain Directus MCP Server actions with LangChain

The `list_schema_collections` tool inspects your PostgreSQL tables so your LangChain agent knows exactly what data structure it is working with. From there, the agent feeds those table names into `list_collection_fields` to map out the columns without you writing a single line of boilerplate SQL. LangChain tracks these multi-step schema discovery runs inside LangSmith. You see the precise inputs and outputs of every single schema inspection tool call in your terminal.

Automate record mutations in active pipelines

Writing highly-available JSON payloads directly into your Directus rows, the `create_cms_record` tool handles new entries instantly. If a previous step in your LangChain graph flags a missing entry, the agent executes this tool to write the data and immediately passes the new row UUID to the next node. For updates, the agent uses `patch_cms_record` to modify specific database values by their ID. The chain runs these mutations sequentially, ensuring your database state matches your external API data.

Inspect media assets and admin directories

To extract structural metadata and verify media storage, the `list_directus_files` tool queries your active assets. Your LangChain agent calls this tool to audit uploaded files, then uses `list_directus_users` to cross-reference which admin identity uploaded them. This setup lets you build automated media compliance chains. The agent runs these checks background-style, flagging unassigned files or unauthorized uploads.

Setup guide

Set up Directus 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 Directus 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({
    "directus-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 Directus transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Directus MCP in LangChain

Use `MultiServerMCPClient` to connect to this server and pull tools like `list_schema_collections`. Pass these tools into `create_agent` so your LangChain agent can query the active PostgreSQL tables dynamically.

Yes, but you should configure strict guardrails. The agent uses `wipe_cms_record` to drop live database rows, which you can monitor in real-time using LangSmith tracing to catch unauthorized deletions.

LangChain manages tool execution sequentially through its graph structure. When calling `patch_cms_record` repeatedly, the MCP Server handles backoff and retry logic at the framework level, preventing database lockouts.

The agent calls `get_collection_details` to verify the table structures before retrieving a row with `get_single_item`.

Your database records and schema details remain inside Vinkius's isolated V8 sandboxes. No data is stored on external servers, and your API tokens never touch the public internet.

Use it with your favorite AI tools

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