How to Use the Typeform MCP in LlamaIndex

Q: How do I find all forms for indexing using Typeform and LlamaIndex?

First, run listforms to get the IDs. Then, you can pass those IDs into subsequent calls like getformdetails, ensuring every piece of metadata gets indexed.

Q: Can I filter responses for RAG purposes with Typeform and LlamaIndex?

Yes. When using listresponses, you can restrict the output by date range or completion status before indexing it, keeping your knowledge base focused on relevant periods.

Q: What is the best way to scope my index with Typeform and LlamaIndex?

Use listworkspaces first. This defines the boundary of the data you want to search, ensuring your vector store stays within a specific organizational context.

Q: Does this MCP Server support real-time updates with Typeform and LlamaIndex?

You can use createwebhook to set up real-time alerts. Your RAG application can then ingest these immediate webhook payloads, keeping the index current.

Ground RAG applications using LlamaIndex.

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Connect Typeform MCP to LlamaIndex

Create your Vinkius account to connect Typeform to LlamaIndex 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 Typeform with LlamaIndex

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Index collected user responses

The `list_responses` tool retrieves all submitted records for a Typeform. By feeding this structured output into your index, you build a knowledge base of past interactions. Your RAG application can then answer questions like 'What was the most common concern mentioned in Q3?' without needing live access to the API.

Determine form schema details

You use `get_form_details` to pull the full structure for a specific Typeform. This detail, including question types and required fields, becomes part of your indexed knowledge. This lets you query not just the answers, but the *rules* that governed those answers, making your search highly precise.

Map out system configurations

The `get_workspace_details` tool gives a full view of all forms and settings within an account's workspace. This metadata can be indexed alongside document chunks. Indexing this allows your LlamaIndex application to answer questions about the overall state, such as 'Which form is located in the Marketing workspace?'

Setup guide

Set up Typeform MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Typeform MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Typeform tools.",
)
response = await agent.run("List recent Typeform data")

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Common questions about Typeform MCP in LlamaIndex

First, run `list_forms` to get the IDs. Then, you can pass those IDs into subsequent calls like `get_form_details`, ensuring every piece of metadata gets indexed.

Yes. When using `list_responses`, you can restrict the output by date range or completion status before indexing it, keeping your knowledge base focused on relevant periods.

Use `list_workspaces` first. This defines the boundary of the data you want to search, ensuring your vector store stays within a specific organizational context.

You can use `create_webhook` to set up real-time alerts. Your RAG application can then ingest these immediate webhook payloads, keeping the index current.

The core data type touched is collected responses. By indexing `list_responses` output, you ground your knowledge base in raw user-submitted data.

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