How to Use the Formbricks MCP in LangChain

Q: Can my LangChain agent read Formbricks responses?

Yes. The agent calls listresponses and filters by survey ID. It then extracts the exact text users submitted.

Build automated user feedback pipelines with LangChain by chaining Formbricks survey triggers and response data.

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Connect Formbricks MCP to LangChain

Create your Vinkius account to connect Formbricks 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 Formbricks with LangChain

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Chain Formbricks Data with LangChain

LangChain agents use `list_responses` and `get_person` to pull user feedback directly into reasoning pipelines. You build a chain that grabs a low NPS score, checks the user profile, and feeds that context into your next step. The output of one tool becomes the input for another. Your agent spots a frustrated user, runs `list_action_classes` to see what they clicked before failing, and logs the entire flow in LangSmith for your product team to review.

Trigger Contextual Surveys

Your ReAct agent calls `create_survey` the exact moment a specific condition is met in your workflow. If a user hits an error state three times, the agent automatically deploys a targeted feedback modal. It cleans up after itself. Once the agent verifies the feedback using `list_surveys` and `get_survey`, it can execute `delete_survey` to remove the prompt and prevent survey fatigue.

Monitor Webhook Infrastructure

Agents maintain their own feedback loops by executing `list_webhooks` to ensure your Formbricks instance is correctly routing data. You don't have to manually check if the connection broke. When combined with `list_people`, your LangChain setup autonomously cross-references active users against active webhooks. You get full observability over who sees your prompts and where that data goes via the MCP Server.

Setup guide

Set up Formbricks 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 Formbricks 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({
    "formbricks-alternative-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 Formbricks 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 Formbricks. 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 Formbricks MCP in LangChain

Use MultiServerMCPClient with your Vinkius endpoint URL. Call client.get_tools() and pass the returned list directly to your create_agent setup.

Yes. The agent calls list_responses and filters by survey ID. It then extracts the exact text users submitted.

LangSmith automatically traces every MCP tool execution. You see exactly how long a create_survey call takes and how many tokens the agent used deciding to run it.

They do. The MCP Server configures the backend logic, while your existing Formbricks frontend snippet handles the actual UI display based on the action classes your agent defines.

Vinkius runs this MCP Server in an ephemeral V8 Isolate Sandbox. When your agent pulls user profiles via get_person, the data passes through a zero-trust environment that terminates immediately after the request finishes.

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