How to Use the Financial Ratios Calculator MCP in LlamaIndex

Index corporate financial health metrics directly into your LlamaIndex knowledge base.

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Connect Financial Ratios Calculator MCP to LlamaIndex

Create your Vinkius account to connect Financial Ratios Calculator to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Financial Ratios Calculator with LlamaIndex

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Index liquidity ratios for semantic search

This MCP uses `get_short_term_liquidity_ratios` to pull liquidity metrics and store them directly in your vector database. Raw numbers are useless if you cannot find them later. Your agent queries this indexed data during future runs. Instead of recalculating, your RAG pipeline finds the historical quick ratio to answer complex qualitative questions.

Build RAG pipelines with solvency data

Your agent calls `get_long_term_solvency_ratios` to capture debt structures and feed them into your index. Solvency metrics provide the foundation for long-term credit analysis. This connects live API data with your static financial PDFs. Your LlamaIndex application synthesizes both sources to generate grounded investment reports without hallucinations.

Evaluate debt service capabilities in LlamaIndex

The `get_interest_coverage_ratio` tool calculates the exact multiplier of operating income to interest expenses. Checking if a company can pay its bills is step one in any credit audit. The math is simple: if the coverage is thin, the model flags the risk. LlamaIndex stores this result so your agent can reference it during subsequent portfolio reviews.

Setup guide

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

Use the LlamaIndex MCP tool spec to wrap this MCP. Pass the resulting tool list to your FunctionAgent to start calculating ratios.

Yes. The tool outputs are structured data that LlamaIndex indexes into your vector store. You can then run semantic searches over past financial calculations.

This MCP handles the formulas out of the box. Your LlamaIndex agent simply calls the tools to get standard accounting metrics, combining them with unstructured text.

The tools expect specific financial inputs. If your agent passes incomplete balance sheet data, the MCP returns a clear error state so your pipeline doesn't break.

All calculations occur in an isolated execution environment. Your balance sheet inputs and financial ratios are never logged, stored, or used for model training.

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