How to Use the Expensya MCP in LlamaIndex

Index your financial records into LlamaIndex for grounded, searchable expense data.

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Connect Expensya MCP to LlamaIndex

Create your Vinkius account to connect Expensya 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 Expensya with LlamaIndex

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Build a searchable index of Expensya data

Pipe the output of `list_expenses` directly into your vector store for instant retrieval. Your index becomes a living record of every transaction. This lets you query your financial history using natural language. You get answers based on your actual data, not generic summaries.

Query Expensya reports with LlamaIndex

Use `list_expense_reports` to pull folder structures into your RAG application. You can now ask questions about specific budget periods or departments. Your index links these reports to individual transactions. You gain a clear view of where money goes without digging through folders.

Sync Expensya categories and tags

Keep your knowledge base current by indexing `list_expense_categories` and `list_expense_tags`. Your agent stays aware of every label currently in use. This keeps your semantic search results accurate. You avoid the confusion of outdated terminology when searching your archives.

Setup guide

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

It treats tool outputs as documents. These are embedded into your vector store for semantic search capabilities.

Absolutely. Once the data is indexed, you can ask about any past expense report by date, user, or category.

Yes. By indexing the live API data, the model answers questions based on your real records instead of guessing.

You can persist your index to disk. This ensures your financial data stays ready for queries even after you restart.

Your expense records and receipt metadata are processed only within your local index. No third party sees your raw financial inputs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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