About Financial Modeling Prep (fmpsdk)
fmpsdk is a Python SDK for the Financial Modeling Prep (FMP) API, covering about 240 methods across 29 data groups. It ships with a separate MCP server, fmpsdk-mcp, for AI assistants, and is intended for developers who need financial statements and market data.
Key features
- 0129 data groups with about 240 methods
- 02Namespaces grouped by data category
- 03Financial statements, ratios and valuation data
- 04Quotes, historical prices and technical indicators
- 05fmpsdk-mcp with 10 curated and discovery tools
- 06Errors raised for calls outside the FMP plan tier
Use cases
- 01Fetching company financial statements and quotes in Python scripts
- 02Letting an AI assistant query quotes and ratios over MCP
- 03Downloading whole-universe data in bulk instead of per symbol
MCP support
The mcp/ directory ships a separate MCP server, fmpsdk-mcp, which exposes 10 curated tools to MCP clients and can reach all roughly 240 methods at runtime.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx fmpsdk-mcpFMP_API_KEYSecretFinancial Modeling Prep API key (https://site.financialmodelingprep.com/developer/docs)Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
13Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| symbol | string | Yes |
Details
From the project READMEFMP SDK
The idea behind this project is to provide a 'one-stop-shop' to the API endpoints provided by Financial Modeling Prep (FMP).
A personal note to you: my apologies for letting this package get so out of date. FMP kept
reshuffling its API, then I had a personal issue delay me further. Things are back on track now.
Because of that gap, roughly half of this package's old methods had gone defunct against FMP's
current API, so rather than patch around that I rebuilt it from scratch against FMP's stable/
API. If you're on 20250102.0 or earlier you're on the old, pre-rewrite methods; anything newer
uses the schema described below. Old releases are yanked from PyPI (a plain pip install fmpsdk
now always gets the rewrite), but still installable if you pin one explicitly, e.g.
pip install fmpsdk==20250102.0 — note that won't actually restore functionality, though: FMP
sunset the legacy /api/v3/ API those old methods called entirely on 2025-08-31, so a pinned old
version installs fine but its calls will fail regardless. It's there for compatibility with
existing pinned requirements files while you migrate, not as a way to keep avoiding the rewrite.
What's covered
29 data groups, ~240 methods total, each reachable both as client.<method>(...) and grouped
under a matching namespace, e.g. client.statements.income_statement(...):
- Company & fundamentals — profile, executives, M&A, DCF valuation, financial statements/ ratios/growth (income, balance sheet, cash flow — as-reported, TTM, and growth variants)
- Market data — real-time & aftermarket quotes, historical price charts, technical indicators
- Reference & screening — symbol/CIK/CUSIP/ISIN search, the company screener, sector/ industry/exchange directories
- Calendars & events — earnings, dividends, splits, IPOs, the economic calendar
- Ownership & compliance — insider trades, Form 13F institutional ownership, SEC filings, ESG disclosures
- Alternative markets — crypto, forex, commodities, indexes, ETFs & mutual funds
- Sentiment & analysis — analyst grades/price targets, TipRanks ratings, congressional trading, market movers
- Bulk downloads — whole-universe data dumps, one call instead of one per symbol
Every method's own docstring says which FMP plan tier it needs (Free/Starter/Premium/
Ultimate) — see Pricing tiers below. One exception: the 7
client.tipranks methods are implemented and unit-tested but untested against a real
response — see that section for why.
How to Use
- Requires Python 3.9+. Install the package:
pip install fmpsdk python-dotenv(python-dotenvis optional — it's just how the example below loads your API key from a.envfile). - Create a
.envfile and put your API key in it. Inside.env:FMP_API_KEY='blah' - Build a
Clientand call methods on it, grouped by data category — e.g.client.company.profile(symbol="AAPL"),client.statements.income_statement(symbol="AAPL"). - The return from a method call is almost always a list of dictionaries. It is up to you to parse it.
Example code
#!/usr/bin/env python3
from dotenv import load_dotenv
import fmpsdk
# Actual API key is stored in a .env file. Not good to store API key directly in script.
load_dotenv()
client = fmpsdk.Client() # reads FMP_API_KEY from the environment
# Company Valuation Methods
symbol = "AAPL"
print(f"Company Profile: {client.company.profile(symbol=symbol)}")
# Every non-2xx FMP response raises a typed exception instead of returning
# None or an error-shaped dict, so a plan-gated or bad-key call is obvious:
try:
client.statements.income_statement_ttm(symbol=symbol)
except fmpsdk.FMPPlanLimitError:
print("Your FMP plan doesn't cover this endpoint — check its docstring for which tier does.")
except fmpsdk.FMPAuthenticationError:
print("Check your FMP_API_KEY.")
One naming quirk worth knowing up front: client.quote is the namespace for the whole quote
group (client.quote.aftermarket_quote(...), client.quote.batch_quote(...), etc.), so it
shadows the top-level quote() method of the same name — call that one as
client.quote.quote(symbol="AAPL").
Pricing tiers
FMP's plans form a ladder — Free, Starter, Premium, Ultimate — and each one adds more
endpoints on top of the last. A call your plan doesn't cover raises
fmpsdk.FMPPlanLimitError rather than returning data. There's no single tier table to keep
in sync here — instead, every group module's docstring (e.g. fmpsdk/endpoints/search.py)
and every method's own docstring says plainly which tier it needs, right next to the code
that calls it, verified live against a real key at every tier from Free through Ultimate.
Check those, or just try the call and catch FMPPlanLimitError.
One exception, outside that ladder entirely: client.tipranks's 7 methods. They still
402 even on Ultimate — FMP's own error message says why: TipRanks data needs a separate
paid add-on ("TipRanks data boost"), bought independently of the four plan tiers above. We
haven't purchased it, so these 7 methods are implemented and unit-tested (mocked) but
never verified against a real response — treat them as unverified rather than
confirmed-working. If you have that add-on and hit a bug in one of them, a PR with the fix
(and what the real response actually looks like) is welcome.
MCP server & OpenAPI spec
mcp/ — a Model Context Protocol
server, published separately as fmpsdk-mcp (so
pip install fmpsdk never pulls in fastmcp). Lets an AI assistant (Claude Code, Claude
Desktop, …) query FMP through this library: 10 curated tools for the common asks (quotes,
profiles, statements, ratios, price history, news) plus discovery tools that reach all
~240 methods at runtime. With uv:
claude mcp add fmpsdk -s user -e FMP_API_KEY=your-key -- uvx fmpsdk-mcp
See mcp/README.md for the Claude Desktop config, the full tool list, and
development setup.
openapi.json — an OpenAPI 3.1 description of the same API surface.
FMP publishes no OpenAPI/Swagger document; this file is generated by introspecting this
library's source (python tools/gen_openapi.py), not fetched from or endorsed by FMP.
Contributing
See CONTRIBUTING.md — dev setup, the unit/live/ultimate test tiers, and
specifically how to verify and PR a fix for the untested tipranks methods above.
License
BSD 3-Clause — see LICENSE.md.
Attribution
Special thanks to the following people who have pitched in on this project! Open source works thanks to people who jump in and help! These are this project's stars. Thank you.
- Ken Caruso
- iforgotmypass
- Ivelin Ivanov
- Claude Code
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.