How to Use the AidaForm MCP in LlamaIndex

Q: Can LlamaIndex export all AidaForm responses at once?

Yes, the agent can trigger exportresponses to grab bulk data. It then chunks and embeds those records into your vector database for fast retrieval.

Q: How do I restrict LlamaIndex from deleting AidaForm data?

Use the allowedtools filter when configuring your MCP tool spec. You can expose read-only operations while completely blocking access to deleteform or deleteresponse.

Index your survey responses directly into a searchable vector store with LlamaIndex.

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Connect AidaForm MCP to LlamaIndex

Create your Vinkius account to connect AidaForm 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 AidaForm with LlamaIndex

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MCP Server Semantic Search

Standard RAG setups rely on static documents. This AidaForm MCP Server changes that by letting your agent pull live survey data via `list_responses` and index it immediately. You query your knowledge base for customer sentiment, and LlamaIndex finds the exact quotes. It grounds its answers in actual feedback rather than guessing what your users might think.

Continuous Analytics Ingestion

Building a dashboard takes time. Just point your RAG application at `get_form_analytics` and let it absorb the conversion metrics directly into your vector store. Managers can ask questions like 'Which quiz had the highest drop-off rate last week?' The system retrieves the numbers, compares them against historical indexed data, and gives a factual answer.

Dynamic Form Auditing

Keeping track of what questions you asked across dozens of campaigns gets messy. Your agent runs `list_forms` and `get_form` to pull the exact schema of every active survey. Those schemas become part of your searchable index. When someone asks if you already collected email addresses in the Q3 feedback round, the agent checks the indexed structure and confirms.

Setup guide

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

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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 LlamaIndex

Run pip install llama-index-tools-mcp. Setup a basic MCP client with your Vinkius URL, wrap it in McpToolSpec, and pass the async tool list to your RAG agent.

Yes, the agent can trigger export_responses to grab bulk data. It then chunks and embeds those records into your vector database for fast retrieval.

You have to write the update logic. Schedule your agent to call check_aidaform_status and fetch new records periodically to keep the vector store fresh.

Use the allowed_tools filter when configuring your MCP tool spec. You can expose read-only operations while completely blocking access to delete_form or delete_response.

Your questionnaire replies only pass through the ephemeral Vinkius endpoint before landing in your private LlamaIndex vector store. The connection is zero-trust, meaning no third party stores your raw text.

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