Are API-created contacts exactly the same as UI-created ones?

Yes contextually. When you execute the `create_contact` function natively alongside `category` parameters (`3` stands for customer, `4` for typical suppliers), sevDesk anchors these entries officially inside the identical primary table. Be clear about whether it's an email-lead versus a firm.

Wait, does my generated API Token expire if left unused?

No. The verified 32-character hexadecimal key token possesses an infinite lifeline inherently tied directly, securely, and stubbornly to your primary login account. It decays or dies primarily upon deliberate manual revocation (regeneration) directly executed by you inside your dashboard user settings.

Can I fetch detailed line items from a single invoice ID using this agent?

Absolutely. Querying `list_invoices` outputs high-level paginated arrays, but invoking the precision extractor `get_invoice` fed deliberately with a target identifier ID unveils the core metadata array detailing isolated tax mappings, due-date boundaries, and intricate line items.

sevDesk MCP Server for LlamaIndex 15 tools — connect in under 2 minutes

Name: sevDesk
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 15 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add sevDesk 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 sevDesk. "
            "You have 15 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in sevDesk?"
    )
    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 sevDesk MCP Server

Grant your conversational AI (like Claude or Cursor) the power of a dedicated German accounting clerk. The sevDesk MCP transforms your LLM into a sophisticated financial nexus capable of creating invoices, querying past-due credit notes, recording vouchers, and tracking your CRM endpoints dynamically. Stop wrestling with browser-based accounting dashboards and let your AI manage your bookkeeping automatically.

LlamaIndex agents combine sevDesk tool responses with indexed documents for comprehensive, grounded answers. Connect 15 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

Client & Supplier CRM Mastery — Read the entire client database via list_contacts, retrieve detailed metadata for specific IDs (get_contact), or seamlessly register brand new vendors via create_contact
Autonomous Invoicing & Orders — Fetch paginated ledger lists using list_invoices or list_orders, and delve deep into line items, calculated taxes, and due statuses directly using get_invoice and get_order
Inventory & Service Architecture — Monitor available billable components via list_parts, inspect standard pricing, and introduce novel catalog options autonomously via create_part
Accounting Governance — Keep strict tabs on your chart of accounts with list_accounting_types while verifying associated business inlets securely using list_bank_accounts
Voucher & Credit Auditing — Consolidate physical receipts through list_vouchers and trace structural adjustments flawlessly by scanning list_credit_notes

The sevDesk MCP Server exposes 15 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 sevDesk to LlamaIndex via MCP

Follow these steps to integrate the sevDesk 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 15 tools from sevDesk

Why Use LlamaIndex with the sevDesk MCP Server

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

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

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

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

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

sevDesk + LlamaIndex Use Cases

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

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

Data enrichment: query sevDesk 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 sevDesk for fresh data

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

sevDesk MCP Tools for LlamaIndex (15)

These 15 tools become available when you connect sevDesk to LlamaIndex via MCP:

create_contact

Category 3 for customers, 4 for suppliers. Creates a new contact (customer or supplier) in sevDesk

create_part

Creates a new part or service in the catalog

get_contact

Retrieves details for a specific contact

get_credit_note

Retrieves details for a specific credit note

get_invoice

Retrieves details for a specific invoice, including line items and tax

get_order

Retrieves details for a specific order

get_part

Retrieves details for a specific part

list_accounting_types

Lists all accounting types (Chart of Accounts)

list_bank_accounts

Lists company bank accounts

list_contacts

Lists all contacts (customers, suppliers) in sevDesk

list_credit_notes

Lists all credit notes

list_invoices

Lists all invoices with embedded contact data

list_orders

Lists all sales orders

list_parts

Lists all parts (products and services) in the catalog

list_vouchers

Lists all vouchers (incoming/outgoing receipts)

Example Prompts for sevDesk in LlamaIndex

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

"Extract the details of invoice #18742 and summarize its line items alongside the associated contact name."

"List all currently existing bank accounts natively integrated using the core config."

Troubleshooting sevDesk MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

sevDesk + LlamaIndex FAQ

Common questions about integrating sevDesk 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 sevDesk tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.