How to Use the Finmei MCP in LlamaIndex

Index your business finances into LlamaIndex for RAG-powered insights and semantic analysis of your expense history.

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Connect Finmei MCP to LlamaIndex

Create your Vinkius account to connect Finmei 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 Finmei with LlamaIndex

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Build knowledge bases with Finmei and LlamaIndex

Automatically ingest results from `list_expenses` and `list_payments` into your vector store. This lets you perform semantic searches across your entire financial history to find specific transactions by description or amount. Your agents can query this knowledge base to answer questions about your spending habits, grounded in the actual data returned by the Finmei MCP server.

Ground AI answers in your Finmei ledger

Use `get_expense` and `get_payment` to provide context for your RAG applications. By combining live API data with indexed documents, you prevent your agent from hallucinating details about your financial records. The system keeps your index up to date by running periodic tool calls, ensuring that your agent always has access to the latest tax rates and currency lists.

Query financial data with LlamaIndex agents

Deploy an agent that uses the Finmei tool spec to look up business profile details via `get_profile` whenever it needs to verify account settings. This creates a unified interface where your agent understands both your documents and your live financial state. You can filter which tools are available to the agent, ensuring it only uses the operations required for your specific RAG task.

Setup guide

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

The MCP tools fetch your live data, which you then pass into your ingestion pipeline. LlamaIndex stores these results as nodes, making them searchable alongside your existing documentation.

Yes. By indexing your expense history, you can ask your agent questions about past spending, and it will use the tools to retrieve the necessary records.

It is. You convert the MCP tools into a tool list that the agent uses to decide when to fetch real-time data versus when to rely on indexed information.

You can set up your agent to refresh the index periodically by triggering the list tools. This keeps your semantic search results synced with your actual financial ledger.

Your expense and payment records are fetched directly from the server. The data is processed within your local LlamaIndex environment, and only the information you index is stored for semantic retrieval.

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