How to Use the Typebot MCP in LlamaIndex

Q: What if I need to track multiple Typebot workspaces?

You can list all accessible Typebot workspaces using listworkspaces. LlamaIndex indexes these names and structures, allowing your agent to maintain a unified map of available resources.

Q: How does Typebot integrate with LlamaIndex for data analysis?

The listtypebotresults tool provides the raw collected user responses. Your LlamaIndex application ingests this data, creating a searchable index that lets you ask complex questions about user behavior.

Q: Can I use LlamaIndex to audit bot changes?

Absolutely. By combining listtypebots and gettypebotdetails, your agent can build a timeline of structural changes, which LlamaIndex then indexes for easy historical retrieval.

Ground answers in history: Typebot and LlamaIndex together for knowledge-augmented AI.

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Connect Typebot MCP to LlamaIndex

Create your Vinkius account to connect Typebot 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 Typebot with LlamaIndex

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Building Searchable Bot Configurations

LlamaIndex indexes bot data, making it searchable. Using `get_typebot_details` means you can query the internal structure of any Typebot logic and get answers grounded in actual API documentation, not just general knowledge. Instead of reading a giant JSON dump, your RAG application searches for 'Where is the lead capture field defined?' and gets a direct, indexed answer.

Querying Past User Interactions

The `list_typebot_results` tool gathers all historical user responses. LlamaIndex indexes this data into a vector store. You can then ask questions like, 'How many times did users mention pricing in the last month?' and get an answer based on indexed records. This turns raw bot logs into a powerful knowledge base you can query semantically.

Indexing Bot Deployment Metadata

Want to know when a specific bot was updated? Indexing the output of `list_typebots` allows your application to search for metadata like 'Which bots were last published on Tuesday?' This capability is huge: it lets you combine operational data (when things changed) with business context.

Setup guide

Set up Typebot 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 Typebot 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 Typebot tools.",
)
response = await agent.run("List recent Typebot data")

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Common questions about Typebot MCP in LlamaIndex

LlamaIndex takes structured output from MCP tools—like the results of `list_typebots`—and embeds that data. It builds an index so your agent can search *within* the bot's available information, making it highly contextual.

You can list all accessible Typebot workspaces using `list_workspaces`. LlamaIndex indexes these names and structures, allowing your agent to maintain a unified map of available resources.

The `list_typebot_results` tool provides the raw collected user responses. Your LlamaIndex application ingests this data, creating a searchable index that lets you ask complex questions about user behavior.

Absolutely. By combining `list_typebots` and `get_typebot_details`, your agent can build a timeline of structural changes, which LlamaIndex then indexes for easy historical retrieval.

The MCP Server touches collected user responses. When indexing this data, your agent ensures that the search context is limited to the specific, consented records you need for RAG purposes.

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