How to Use the Billit MCP in LlamaIndex

Index your Billit financial records into LlamaIndex to create a searchable knowledge base for your RAG applications.

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Connect Billit MCP to LlamaIndex

Create your Vinkius account to connect Billit 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 Billit with LlamaIndex

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Query Billit records in LlamaIndex

Use `list_parties` to pull customer data into your vector index. LlamaIndex turns these lists into semantic search targets for your RAG agents. Your model stops hallucinating about client history. It queries the actual index populated by your live Billit account data.

Search expenses within LlamaIndex

Ingest output from `list_expenses` to make your spending history queryable. You can ask your agent specific questions about historical costs. This creates a grounded knowledge layer. The agent references your real financial history to provide accurate, factual summaries.

Index product catalogs in LlamaIndex

Call `list_products` to build a vector store of your items. Your agent uses this index to look up SKU details before drafting new invoices. It removes the need for hardcoded product lists. Your search results are always current because they pull directly from your production data.

Setup guide

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

Use the MCP tool spec to convert the server tools into a format LlamaIndex understands. The agent then dynamically calls these functions to gather data for your index.

Your vector store holds the data based on your configuration. The MCP server acts as the live bridge to ensure your index updates when the API changes.

It provides the raw data for your retrieval steps. You can mix these tool outputs with your existing documents to create a unified knowledge source.

Yes, you can define an allowed tools list to restrict what the agent can access. This keeps your RAG application focused on specific accounting tasks.

Your financial figures and private customer names are processed through a secure, ephemeral tunnel. Vinkius ensures your data doesn't persist beyond the current session.

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