How to Use the Evoliz Invoicing & Management MCP in LlamaIndex

Q: Can I query overdue invoices using LlamaIndex?

Yes, by calling listunpaidoverdueinvoices and indexing the output. Your agent then answers questions about which clients have outstanding balances.

Index your Evoliz financial records in LlamaIndex. Build RAG apps that query live invoices and client profiles.

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Connect Evoliz Invoicing & Management MCP to LlamaIndex

Create your Vinkius account to connect Evoliz Invoicing & Management 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 Evoliz Invoicing & Management with LlamaIndex

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Index live invoice data for LlamaIndex

Fetch invoice history via `list_sales_invoices` and feed it into your vector store. LlamaIndex turns these records into searchable context for your agent. This setup allows you to query your financial history using natural language. The agent retrieves exact invoice details from `get_invoice_detailed_data` whenever it needs to ground a response.

Searchable CRM client profiles

Index client data retrieved from `list_crm_clients` to build a semantic knowledge base. You can ask your agent about specific client payment behaviors or history. LlamaIndex maps this data so your agent finds relevant client info instantly. It stops the agent from guessing by providing direct access to your actual CRM records.

Unified financial knowledge base

Combine `list_sales_quotes` and invoice data into one queryable index. Your agent uses this base to answer complex questions about your current financial state. You avoid hallucinations by forcing the agent to reference the data it pulls from the server. It queries the index, not its internal memory.

Setup guide

Set up Evoliz Invoicing & Management 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 Evoliz Invoicing & Management 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 Evoliz Invoicing & Management tools.",
)
response = await agent.run("List recent Evoliz Invoicing & Management 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 Evoliz Invoicing & Management. 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 Evoliz Invoicing & Management MCP in LlamaIndex

Use the MCP client to fetch tool definitions and pass them to your agent. Once active, the agent pulls data into the vector store for indexing.

It acts as a live data source for your RAG applications. You configure the agent to call tools as needed to refresh the indexed information.

We enforce strict data isolation within the Vinkius sandbox. Only authorized calls to specific endpoints like `get_client_detailed_profile` are permitted during your session.

Yes, by calling `list_unpaid_overdue_invoices` and indexing the output. Your agent then answers questions about which clients have outstanding balances.

Your invoice data is ephemeral. It resides in the vector store only for the duration of the session and is wiped once the connection closes.

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