How to Use the OpnForm MCP in LangChain

Q: How do I install the OpnForm integration for LangChain?

Run pip install langchain-mcp-adapters langgraph in your terminal. Then initialize it using MultiServerMCPClient pointing to the Vinkius MCP endpoint.

Q: Can LangChain agents create new OpnForm forms autonomously?

Yes. Once you pass client.gettools() to your agent, it can invoke the createform tool. You just need to provide the target workspace ID.

Q: How do I fetch specific form responses?

Have your agent call getsubmission with the exact submission ID. It returns the raw JSON payload for that specific entry.

Build multi-step LangChain pipelines to generate and manage OpnForm workspaces directly from your ReAct agents.

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…and any MCP-compatible client

MCP Servers — Included with Plan

Connect OpnForm MCP to LangChain

Create your Vinkius account to connect OpnForm to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup OpnForm with LangChain

Ask AI about this MCP

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Claude

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Connect OpnForm to LangChain

You need an agent that handles OpnForm creation and data retrieval without breaking context. By connecting this MCP Server, your ReAct agents can hit `list_workspaces` and map out your current setup before doing anything else. The real utility comes from chaining these operations. You can pull data with `get_me` to verify permissions, then immediately fire off a `create_form` request using the previous step's output as the payload.

Process Form Submissions in Pipelines

The `list_submissions` tool pulls recent entries from a specific form ID so you can actually do something with the collected data. Forms collect information, but your agent dictates where it goes. Once LangChain has that payload, you can route the data into your vector stores or trigger downstream APIs. If a submission contains spam, your agent can call `delete_submission` to keep the database clean.

Full Observability on Form Actions

The `update_form` tool modifies live workspaces, which means you need to know exactly what payload your agent sent. LangSmith tracing captures the input and output for every single MCP tool invocation. If a `delete_form` execution fails because of a missing ID, you see the exact error response. You don't have to guess why the pipeline stalled.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by OpnForm. 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.

Why Choose Vinkius

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Common questions about OpnForm MCP in LangChain

Run `pip install langchain-mcp-adapters langgraph` in your terminal. Then initialize it using `MultiServerMCPClient` pointing to the Vinkius MCP endpoint.

Yes. Once you pass `client.get_tools()` to your agent, it can invoke the `create_form` tool. You just need to provide the target workspace ID.

It does if you enable LangSmith. You get full visibility into the execution times and payload sizes for every MCP tool call.

Have your agent call `get_submission` with the exact submission ID. It returns the raw JSON payload for that specific entry.

This MCP Server runs in an ephemeral V8 Isolate Sandbox on Vinkius. When your agent pulls form answers or file upload metadata, the data routes directly to your client. We destroy the execution environment immediately after the request finishes, leaving zero residual data.

Use it with your favorite AI tools

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