How to Use the Mokaform MCP in LlamaIndex

Q: Can LlamaIndex update my Mokaform templates based on user queries?

Your agent can evaluate query trends and run updateform to add relevant questions to your surveys. This keeps your questionnaires aligned with what users are actually asking.

Q: How does semantic search work with my form schemas?

LlamaIndex indexes the output of getform as document nodes. This allows your agent to find the correct form to update by searching for natural language descriptions.

Index live survey responses into your LlamaIndex vector store using the Mokaform MCP Server.

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Connect Mokaform MCP to LlamaIndex

Create your Vinkius account to connect Mokaform 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 Mokaform with LlamaIndex

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Index live survey responses for RAG

The `get_response` tool retrieves specific user answers so your LlamaIndex pipeline can index them into a vector store. This turns raw survey submissions into searchable knowledge bases for your semantic search queries. Your agent queries past configurations and gets answers grounded in real data, avoiding typical hallucinations. It matches new user queries against historical answers retrieved via `list_responses` to find patterns.

Query form structures using LlamaIndex MCP Server tools

The `get_form` tool extracts form structures and conditional branching logic directly into your index. Your agent reads this metadata to understand the exact questions and paths available in your surveys. If you need to modify a path, the agent checks the structure, identifies the gap, and executes `update_form` to fix the branching. You keep your forms aligned with your documentation without manual schema mapping.

Organize workspace data for retrieval-augmented generation

The `list_workspaces` tool provides your LlamaIndex agent with a complete map of your active workspaces. The agent uses this list to filter document retrieval by specific teams or survey projects. When cleanups are required, the agent calls `delete_response` to remove old records from both the form host and your vector index. This ensures your retrieval pipeline never references stale user data.

Setup guide

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

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Common questions about Mokaform MCP in LlamaIndex

You initialize the MCP client with your Vinkius URL and convert it to a tool spec. The LlamaIndex agent calls `list_responses` to fetch data and inserts it directly into your document index.

Your agent can evaluate query trends and run `update_form` to add relevant questions to your surveys. This keeps your questionnaires aligned with what users are actually asking.

Yes, you can call the asynchronous tool listing methods to load tools into your FunctionAgent. This prevents blocking your main query pipeline when fetching large forms.

LlamaIndex indexes the output of `get_form` as document nodes. This allows your agent to find the correct form to update by searching for natural language descriptions.

Vinkius routes your MCP calls through isolated V8 sandboxes that handle authorization tokens securely. Your raw survey configurations and questions are never cached on the proxy layer.

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