How to Use the AidaForm MCP in LangChain

Q: Can my LangChain agent read AidaForm survey answers?

Yes. The agent uses the getresponse tool to pull specific answers. It can then pass those text fields into other chains for sentiment analysis.

Build multi-step reasoning pipelines that create and analyze surveys automatically using LangChain.

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Connect AidaForm MCP to LangChain

Create your Vinkius account to connect AidaForm 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 AidaForm with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LangChain Pipeline Generation

Your ReAct agent needs to gather structured user feedback. Instead of stopping to ask for manual form creation, it calls `create_form` directly from your LangChain pipeline. The agent decides the questions based on previous conversation context. Once the survey is live, the chain doesn't break. You can hook `list_responses` into a continuous evaluation loop. Output from the form becomes immediate input for your next LLM prompt.

Automated Analytics Extraction

Forms generate raw data that usually requires human review. By giving your agent access to `get_form_analytics`, it pulls completion rates and drop-off metrics on its own. The real value happens when you pipe that data into LangSmith. You track exactly how many tokens it took to process a thousand survey answers via `export_responses` and calculate your exact cost per insight.

Workspace Lifecycle Control

Agents can manage the entire lifecycle of a feedback loop. They look up available environments using `list_workspaces` and deploy new quizzes right where they belong. When a campaign finishes, the MCP Server handles cleanup. Your chain issues a `delete_form` command, keeping your dashboard completely free of outdated tests.

Setup guide

Set up AidaForm 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 AidaForm 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({
    "aidaform-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 AidaForm 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 AidaForm. 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 AidaForm MCP in LangChain

Install the langchain-mcp-adapters package. Pass your Vinkius MCP endpoint URL into MultiServerMCPClient and call get_tools() to feed them to your ReAct agent.

Yes. The agent uses the get_response tool to pull specific answers. It can then pass those text fields into other chains for sentiment analysis.

The integration is stateless by default. If you want your agent to remember which form it created across multiple turns, use client.session() to maintain that context.

You might be missing the exact form ID. Make sure your pipeline runs list_forms first so the agent can grab the correct identifier before calling update_form.

LangChain processes your raw survey answers in memory during the chain execution. The Vinkius V8 Isolate Sandbox destroys the environment immediately after the run, leaving no trace of your users' personal data.

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