How to Use the Bexio MCP in LlamaIndex

Q: How do I index Bexio data into LlamaIndex using the MCP Server?

Install llama-index-tools-mcp and initialize the client pointing to your Vinkius server URL. Use McpToolSpec to load the tools, then feed the output of listitems or listprojects into your index pipeline.

Q: Can a LlamaIndex agent create records in Bexio?

Yes. The agent can use the createcontact tool to add new clients based on information extracted from your indexed documents or emails.

Q: How do I filter which Bexio tools my LlamaIndex agent can see?

You can use the allowedtools filter when configuring your tool specification. This lets you expose read-only tools like listoffers while hiding creation tools to keep your data safe.

Q: Does LlamaIndex support real-time updates from this server?

Your agent queries live endpoints like listinvoices on demand. Every time the agent runs a step, it retrieves the freshest state directly from your Swiss ERP account.

Q: How does the Bexio and LlamaIndex MCP setup protect my client lists?

All transactions, including contact details retrieved via getcontact, pass through isolated Vinkius containers. Your credentials are never exposed to the LLM, and the ephemeral sandbox ensures that no accounting data is cached or stored after the query completes.

Index Swiss ERP records directly into your LlamaIndex vector store using this secure MCP Server.

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Connect Bexio MCP to LlamaIndex

Create your Vinkius account to connect Bexio 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 Bexio with LlamaIndex

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Build RAG pipelines with live Bexio ERP data

This integration lets you index live data directly from your Swiss business account into your LlamaIndex vector store. Your agent can pull active records using `list_invoices` or `list_orders` and turn them into searchable document nodes on the fly. Instead of relying on static exports, your RAG system queries real-time business operations. When a user asks about outstanding balances, the system pulls current data with `get_invoice` to ground its answers.

Search past Swiss client interactions semantically

By combining `list_contacts` and `get_contact` with LlamaIndex storage, you build a searchable database of your customer relations. You can run semantic queries over your history to find specific client details without exact keyword matching. The agent reads the raw JSON from `list_users` and indexes user roles alongside your project directory from `list_projects`. This context keeps your customer-facing agents accurate and informed.

Use this MCP Server to ground agent responses

Stop LLM hallucinations about your business inventory or pricing. By exposing `list_items` as an indexable tool, your LlamaIndex agent checks actual stock and catalog prices before answering user queries. The `McpToolSpec` wrapper translates these operations into clean LlamaIndex tools. Your `FunctionAgent` can execute these checks dynamically whenever it needs concrete catalog data to complete a task.

Setup guide

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

Install `llama-index-tools-mcp` and initialize the client pointing to your Vinkius server URL. Use `McpToolSpec` to load the tools, then feed the output of `list_items` or `list_projects` into your index pipeline.

Yes. The agent can use the `create_contact` tool to add new clients based on information extracted from your indexed documents or emails.

You can use the `allowed_tools` filter when configuring your tool specification. This lets you expose read-only tools like `list_offers` while hiding creation tools to keep your data safe.

Your agent queries live endpoints like `list_invoices` on demand. Every time the agent runs a step, it retrieves the freshest state directly from your Swiss ERP account.

All transactions, including contact details retrieved via `get_contact`, pass through isolated Vinkius containers. Your credentials are never exposed to the LLM, and the ephemeral sandbox ensures that no accounting data is cached or stored after the query completes.

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