How to Use the FIRE Calculator MCP in LlamaIndex

Index your financial projections with LlamaIndex by grounding your knowledge base in live data from the FIRE Calculator.

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…and any MCP-compatible client

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Connect FIRE Calculator MCP to LlamaIndex

Create your Vinkius account to connect FIRE 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 FIRE Calculator with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Ground your knowledge in live math

Feed the output of `calculate_milestones` directly into your LlamaIndex vector store. This ensures your agent answers questions based on current financial projections rather than outdated documentation. You gain a searchable knowledge base that reflects your actual savings progress. It turns static numbers into queryable context that your agent can reference throughout your research.

Analyze contribution scenarios

Run `simulate_contribution_impact` to generate data points for your knowledge base. LlamaIndex then indexes these results so you can query your agent about how different savings rates affect your timeline. This approach builds a library of financial outcomes that your agent understands semantically. You stop hallucinating future plans and start relying on indexed, verifiable data.

Clarify lifestyle targets

Use `get_lifestyle_targets` to define your retirement needs and store them as indexed facts. Your LlamaIndex agent can then compare these targets against your current portfolio status whenever you ask a question. It brings structure to your financial planning by connecting your goals to actionable data. Your agent becomes a specialized advisor that understands your specific retirement requirements.

Setup guide

Set up FIRE 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 FIRE 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 FIRE Calculator tools.",
)
response = await agent.run("List recent FIRE 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 FIRE 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.

Why Choose Vinkius

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Common questions about FIRE Calculator MCP in LlamaIndex

LlamaIndex captures the tool outputs and indexes them into a vector store. This allows your agent to perform semantic searches over your financial projections.

Absolutely. You can import the MCP tool spec, convert it to a list of functions, and pass them directly to your agent for immediate use.

The MCP handles your inputs locally during the calculation process. No sensitive financial information is kept on our servers after the request is completed.

Yes. You can specify an allowed tools filter to control exactly which financial operations your agent is authorized to perform.

It helps by providing grounded, math-based data for your agent to index. Your agent then relies on these factual outputs rather than making up estimates.

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