How to Use the Paperform MCP in LlamaIndex

Q: How do I set up the Paperform MCP Server with LlamaIndex?

Install llama-index-tools-mcp using pip. Initialize BasicMCPClient with your endpoint URL, convert it to tools with McpToolSpec, and pass them to your FunctionAgent.

Q: Can LlamaIndex index multiple Paperform forms at once?

Yes. Your agent can call listforms to get a complete list, then iterate through each form using listsubmissions to build a unified vector index of all responses.

Q: How does LlamaIndex avoid API rate limits when querying Paperform?

You can use LlamaIndex caching strategies alongside the getform tool. This ensures you only fetch active form schemas when they actually change, saving your API quota.

Q: Can I use LlamaIndex to search through specific Paperform folders?

Yes, you can have your agent query listfolders first, then filter the forms it indexes based on the folder structure of your workspace.

Index Paperform submissions directly into LlamaIndex vector stores for grounded, accurate RAG queries.

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Setup Paperform with LlamaIndex

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Build RAG Pipelines with Paperform Data

LlamaIndex allows you to grab live responses using `get_submission` and index them straight into a vector store via this MCP Server. This means your agent queries actual customer feedback rather than guessing or relying on outdated static exports. By using `list_submissions`, the engine keeps your local index fresh with the latest entries. Your search queries will always return answers grounded in real, active database records.

Organize Forms with the LlamaIndex MCP Server

You can use `list_folders` and `list_tags` to structure how your agent searches through your assets. Instead of dumping all form data into one giant index, the agent categorizes information based on your actual workspace organization. This contextual awareness lets your agent answer complex workspace questions. It can quickly find which custom domain is tied to a specific campaign by querying `list_custom_domains` on demand.

Ground Agent Answers in Real Form Schemas

Before generating an answer, your agent can call `get_form` to inspect the exact questions and field types of your live forms. This prevents the model from hallucinating fields that do not exist in your active setup. The agent uses this schema knowledge to format its search queries accurately. You get highly precise data retrieval because the system understands the structure of your forms before it queries their submissions.

Setup guide

Set up Paperform 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 Paperform 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 Paperform tools.",
)
response = await agent.run("List recent Paperform 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 Paperform. 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 Paperform MCP in LlamaIndex

Install `llama-index-tools-mcp` using pip. Initialize `BasicMCPClient` with your endpoint URL, convert it to tools with `McpToolSpec`, and pass them to your `FunctionAgent`.

Yes. Your agent can call `list_forms` to get a complete list, then iterate through each form using `list_submissions` to build a unified vector index of all responses.

You can use LlamaIndex caching strategies alongside the `get_form` tool. This ensures you only fetch active form schemas when they actually change, saving your API quota.

Yes, you can have your agent query `list_folders` first, then filter the forms it indexes based on the folder structure of your workspace.

Your form submissions and customer contact details remain completely secure. The integration executes within an isolated, zero-trust runtime environment that never caches or exposes your API keys and raw payload data.

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