Where do I find my Quaderno API Key and URL?

Log in to your Quaderno account. Your **API URL** is simply your domain up top in the browser bar (e.g., `https://mycompany.quadernoapp.com`). To get the **API Key**, navigate to **Settings** > **API Keys** (or Developers), and copy your private key. Do not share this key as it carries full access. Paste both credentials below.

Can it calculate exact taxes dynamically before a sale?

Yes! Use the `calculate_taxes` tool and just provide the AI with a country code, postal code, and base amount. Quaderno's world-class engine evaluates real-time location-based tax nuances (VAT, GST) and returns the exact fraction needed.

Can I draft custom test invoices natively in chat?

Absolutely. You can use `create_transaction`. Instruct your agent to format a JSON array with products and amounts, linking to a specific `contact_id`. The server submits this array to Quaderno and records the fully realized transaction safely.

Will deleting a contact via the AI remove them permanently?

Yes. The `delete_contact` operation relies heavily on standard destructive API instructions. Running this means the contact is gone entirely from the Quaderno dashboard. Always review deletion queries cautiously before approving execution.

Quaderno MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Quaderno
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Quaderno as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Quaderno. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Quaderno?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Quaderno MCP Server

Connect your Quaderno account to any AI agent and bring powerful tax compliance, invoicing, and customer management capabilities directly into your automated workflows.

LlamaIndex agents combine Quaderno tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Tax Calculations on the Fly — Instantly determine the accurate sales tax, VAT, or GST based on the customer's region and amount before finalizing sales logic
Invoice Management — Search and retrieve generated invoices, audit billing records, and verify transactions perfectly formatted via intelligent prompts
Generate Transactions — Transact and issue invoices seamlessly by sending a simple JSON array of itemized products and line item prices
Full Contact CRM — Map your users fully by creating, modifying, retrieving, and safely deleting user contacts and billing profiles natively

The Quaderno MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Quaderno to LlamaIndex via MCP

Follow these steps to integrate the Quaderno MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Quaderno

Why Use LlamaIndex with the Quaderno MCP Server

LlamaIndex provides unique advantages when paired with Quaderno through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Quaderno tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Quaderno tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Quaderno, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Quaderno tools were called, what data was returned, and how it influenced the final answer

Quaderno + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Quaderno MCP Server delivers measurable value.

Hybrid search: combine Quaderno real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Quaderno to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Quaderno for fresh data

Analytical workflows: chain Quaderno queries with LlamaIndex's data connectors to build multi-source analytical reports

Quaderno MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Quaderno to LlamaIndex via MCP:

calculate_taxes

Calculates applicable taxes for a potential sale

create_contact

Specify email, first name, and last name. Creates a new contact in Quaderno

create_transaction

Provide the contact ID and a JSON array of items. Records a new transaction and issues an invoice

delete_contact

This action is irreversible. Deletes a contact from Quaderno

get_contact

Retrieves details for a specific contact

get_invoice

Retrieves details for a specific invoice

list_contacts

Lists all contacts (customers) in the Quaderno account

list_invoices

Lists all issued invoices

list_transactions

Lists all recorded transactions

update_contact

Provide a JSON payload with the changes. Updates an existing contact

Example Prompts for Quaderno in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Quaderno immediately.

"Calculate the taxes for a $150 plan sold to a user in Berlin, Germany (Postal Code 10115)."

"Fetch the billing details and history for contact ID #9822."

"Update contact #9822 to change its first name to 'Acorn Group Inc'."

Troubleshooting Quaderno MCP Server with LlamaIndex

Common issues when connecting Quaderno to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Quaderno + LlamaIndex FAQ

Common questions about integrating Quaderno MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Quaderno tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.