First Steps to start with MCP Servers for Financial Data

Your beginner-friendly guide to exploring MCP Server for smarter data accessPhoto by Google DeepMind

If you’ve ever worked with financial data, you know the struggle — multiple REST endpoints, inconsistent formats, and endless rate limits that make even simple analysis feel tedious. Fetching company profiles, ratios, and statements often means juggling dozens of URLs or writing custom scripts to clean it all up.

That’s exactly where Financial Modeling Prep (FMP) steps in with its MCP Server — a smarter, unified way to access structured financial data. Instead of calling APIs one by one, you can connect once and interact with multiple data tools like getCompanyProfile, getFinancialRatiosTTM, and getIncomeStatement directly through a single protocol.

What makes it even better is how seamlessly it integrates with modern AI tools and environments like Cursor, VS Code, or Python notebooks. You can literally type a company name and start analyzing data in seconds — no messy setup, no rate-limit headaches.

In this article, we’ll explore how to take your first steps with FMP’s MCP Server, understand what makes it unique, and see how you can start fetching and visualizing real financial data almost instantly.

What Makes FMP’s MCP Server Special

The Model Context Protocol (MCP) is designed to make data interaction smoother and smarter — and FMP has implemented it beautifully for financial data. Instead of relying on scattered API endpoints, the FMP MCP Server acts as a single, intelligent gateway where every piece of information is neatly structured and ready to use.

Think of it as your all-in-one access point. Whether you want a company’s profile, recent income statement, or up-to-date financial ratios, you don’t need to jump across different APIs. You just connect once and call the right tool — for example, getCompanyProfile for fundamentals or getFinancialRatiosTTM for ratio insights.

The best part? It’s built for flexibility. You can connect it directly inside Cursor, integrate it into your Python scripts, or even use it as a backend for AI-powered dashboards. The data it returns is already structured in clean JSON format — no extra parsing, no wasted time.

By combining real-time accessibility, structured outputs, and AI-ready compatibility, FMP’s MCP Server bridges the gap between traditional data APIs and intelligent automation. It’s the fastest way to turn raw market data into actionable insights.

How to Get Started (The Easy Way)

Getting started with FMP’s MCP Server is surprisingly simple. You don’t need to spend hours setting up APIs or managing credentials — it just works with most modern tools.

If you’re using an AI IDE like Cursor, you’re already halfway there. Cursor natively supports MCP connections, meaning you can add FMP’s MCP server once and instantly start calling tools like getCompanyProfile or getIncomeStatement right from your editor. No extra setup, no manual token exchange.

For those who prefer running it locally, FMP has made it equally effortless. You can spin up the MCP server through Docker using a single command, which automatically loads your API key and available tool sets. Once running, it exposes an endpoint on your machine (usually localhost:8090/mcp/) that you can access from any Python script or LLM agent.

Whether you’re in Cursor or working locally, the connection experience remains the same — you call structured tools instead of raw endpoints. That’s the magic of MCP: one connection, multiple possibilities.

How to build visual chart for Financial Data with MCP and Python

Exploring the Available Tools

Once your FMP MCP Server is connected, the next step is exploring what it can do. Instead of memorizing dozens of API URLs, you now have a collection of prebuilt tools — each designed for a specific type of financial data.

You can list all available tools through your IDE or Python with a simple command like:

{"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}

This returns a clean list of callable tools such as:

getCompanyProfile
getFinancialRatiosTTM
getIncomeStatement
getKeyMetricsTTM

Each of these tools acts like a mini API of its own — but without the complexity. You don’t need to remember parameters or parse responses manually. Just pass a company symbol, and you’ll instantly get structured data ready for analysis.

For instance:

• getCompanyProfile → Gives you fundamentals like sector, CEO, market cap, and price.
• getFinancialRatiosTTM → Returns key ratios like P/E, ROE, and Current Ratio.
• getIncomeStatement → Fetches detailed revenue, gross profit, and net income trends.

With these tools, FMP has made financial data both accessible and actionable — perfect for analysts, developers, or anyone building AI-powered finance solutions.

Fetching Real Financial Data

Now comes the fun part — pulling live data directly through the FMP MCP Server.
Once connected, you can start calling tools using simple JSON-RPC requests or even straight from your IDE. The data you get back is already structured, so it’s instantly usable for analysis or visualization.

Here’s how you can fetch a company’s fundamentals in Python:

data = mcp_call("getCompanyProfile", {"symbol": "AAPL"})
print(data[0]["companyName"], "| Price:", data[0]["price"])

You’ll instantly get Apple’s profile — including its name, stock price, exchange, and sector — all neatly formatted.

Want financial ratios instead? It’s just as easy:

ratios = mcp_call("getFinancialRatiosTTM", {"symbol": "AAPL"})
print(ratios[0]["peRatioTTM"], ratios[0]["returnOnEquityTTM"])

And for deeper insights, you can pull the income statement to analyze revenue trends:

income = mcp_call("getIncomeStatement", {"symbol": "AAPL"})

Each response comes as a clean JSON array that can be converted into a pandas DataFrame with just one line. That means you can instantly create charts, perform comparisons, or even feed the data into machine learning models — without the hassle of raw API handling.

The FMP MCP Server turns what used to be a multi-step process into a single, fluid call — clean, structured, and lightning fast.

Lessons from Experience

After experimenting with FMP’s MCP Server across different setups — Python scripts, Docker, and even AI IDEs like Cursor — a few lessons stand out that can save you hours of debugging.

Always include the right headers

Each request must have "Accept": "application/json, text/event-stream" Without it, responses may appear empty because MCP streams data using Server-Sent Events (SSE).

Don’t force unsupported parameters

Some tools like getIncomeStatement don’t accept parameters such as period or limit. If you include them, you might get an empty or incomplete response. Stick to what’s documented.

Handle missing or NaN values gracefully

Financial data isn’t always complete — use safe lookups like .get() when accessing fields such as peRatioTTM or debtToEquityTTM. Missing fields can easily break your analysis.

Remember the endpoint format

It’s http://localhost:8090/mcp/ — with a trailing slash. This detail matters, especially when making calls programmatically.

Verify your session before making tool calls

Make sure your session is initialized and you’ve captured the Mcp-Session-Id. Without it, subsequent requests will silently fail.

These are small things, but they make all the difference between a smooth MCP workflow and hours of unnecessary troubleshooting. Once you’ve configured it right, the experience feels almost magical — real-time data access with zero friction

The FMP MCP Server changes how we access and interact with financial data. Instead of juggling endpoints or cleaning raw JSON, you now have a structured, intelligent interface that delivers exactly what you need — instantly. It’s fast, developer-friendly, and works seamlessly with Python or modern AI tools like Cursor.

If you’ve been relying on APIs until now, this is your next step forward — one connection, unlimited insights.

First Steps to start with MCP Servers for Financial Data was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.