How to Use the Simple Fatoora MCP in LlamaIndex

Q: How does Simple Fatoora and LlamaIndex manage tax data?

When you call createvatinvoice, the calculated VAT details (rates, amounts) are stored in your index. This means you can query past calculations to verify historical compliance later on.

Q: Can I build a knowledge base using Simple Fatoora and LlamaIndex?

Yes. You run the tools—like createsimplifiedinvoice—and index the full output, including item lists, client details, and tax calculations. Your RAG application can then answer questions about that data.

Q: How do I check if a client exists before billing with Simple Fatoora and LlamaIndex?

You run listclients and index the list result. Later, you query the index to confirm existence, which is more robust than simply calling getclient because it grounds your answer in stored data.

Build RAG knowledge bases for invoicing using LlamaIndex.

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Connect Simple Fatoora MCP to LlamaIndex

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Setup Simple Fatoora with LlamaIndex

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Indexing Invoicing Rules with LlamaIndex MCP Server

The core benefit here is persistence. When you run `create_vat_invoice`, the resulting ZATCA-compliant invoice data doesn't just disappear—it gets indexed. You can later ask your agent, 'What were our VAT rates for client X last month?' and get an answer grounded in the actual API output. This turns transient billing data into a searchable knowledge base. You combine live financial records with internal policy documents all within one queryable index.

Querying Invoice History with LlamaIndex

Need to verify what was billed previously? Run `list_invoices` and store the JSON output. Your LlamaIndex application can then answer complex questions like, 'Which invoices in Q3 used item code 45B?' It pulls details from the raw API data instead of making a new search call. This is ideal for audit prep or reconciling old accounts without manual lookups.

Structured Data Management via LlamaIndex

LlamaIndex helps you treat your client and item lists as indexed knowledge. Instead of just calling `create_client`, you index the resulting client record, making that data part of your general search corpus. You can then combine that stored context with a future invoice request. It's about building applications where API calls are historical facts, not just temporary actions.

Setup guide

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

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Common questions about Simple Fatoora MCP in LlamaIndex

When you call `create_vat_invoice`, the calculated VAT details (rates, amounts) are stored in your index. This means you can query past calculations to verify historical compliance later on.

Yes. You run the tools—like `create_simplified_invoice`—and index the full output, including item lists, client details, and tax calculations. Your RAG application can then answer questions about that data.

You run `list_clients` and index the list result. Later, you query the index to confirm existence, which is more robust than simply calling `get_client` because it grounds your answer in stored data.

It's perfect. Instead of just listing invoices, you query the indexed invoice details—including item JSON strings—to understand exactly what happened months ago without needing direct API access.

The server handles financial records, including transaction amounts, specific line item details, VAT rates used, and the client's identifying information. This is critical account activity data.

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