How to Use the Mokaform MCP in LangChain

Q: Can my LangChain chain update a Mokaform layout dynamically?

Yes, your agent uses updateform to change questions based on intermediate chain outputs. This lets you inject new branching rules on the fly during execution.

Q: Does Mokaform support parallel tool execution in LangChain?

Yes, LangChain handles parallel calls to tools like listforms and listworkspaces simultaneously. The Vinkius sandbox processes these requests in parallel safely.

Q: Can I use LangSmith to trace my Mokaform tool calls?

Every call to getresponse or createform shows up in your LangSmith dashboard automatically. You can inspect the exact payload, latency, and token cost for each tool run.

Build dynamic multi-step survey chains in LangChain using this Mokaform MCP Server.

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Connect Mokaform MCP to LangChain

Create your Vinkius account to connect Mokaform 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 Mokaform with LangChain

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Claude

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Build dynamic survey flows in your LangChain pipelines

The `create_form` tool starts your dynamic survey workflows by letting your LangChain agent generate structured questionnaires on the fly. Your agent reads the user's initial prompt, builds the form schema, and sets up conditional branching logic based on real-time parameters. This setup passes the output of one step directly to the next inside a LangGraph configuration. You get full visibility in your traces when the agent updates the layout using `update_form` to inject new follow-up questions.

Extract response data for multi-step reasoning

The `get_response` tool pulls specific submission details so your LangChain chains can evaluate user input instantly. Instead of dumping raw text, your agent parses the answers to decide which external database or vector store to query next. If a user enters a low satisfaction score, the agent triggers `list_responses` to check historical feedback from that workspace. It builds a contextual troubleshooting loop without manual coding.

Manage workspaces via LangChain MCP Server tools

The `list_workspaces` tool exposes your entire form organization structure to your LangChain agent. The agent scans your workspaces, isolates the targeted forms, and updates them without hardcoded IDs. When a survey gets retired, the agent runs `delete_response` to scrub specific entries and keep your storage clean. You control the entire lifecycle through a single unified interface.

Setup guide

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

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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 Mokaform. 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 Mokaform MCP in LangChain

You install the MCP adapter package and pass the Vinkius HTTP endpoint to the client. The agent gets the tool definitions directly by calling the get tools method.

Yes, your agent uses `update_form` to change questions based on intermediate chain outputs. This lets you inject new branching rules on the fly during execution.

Yes, LangChain handles parallel calls to tools like `list_forms` and `list_workspaces` simultaneously. The Vinkius sandbox processes these requests in parallel safely.

Every call to `get_response` or `create_form` shows up in your LangSmith dashboard automatically. You can inspect the exact payload, latency, and token cost for each tool run.

Vinkius runs the server inside a secure, ephemeral V8 isolate sandbox that never stores your survey configurations or response data. Your credentials stay encrypted, and the connection drops the moment the tool execution finishes.

Use it with your favorite AI tools

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