Finance Pulse: Python client and MCP server

Financial news as structured, sourced data. Finance Pulse reads financial news as it is published and turns every article into statements: one-sentence facts with sentiment, importance, tickers, sector, source and publication time. Statements about the same development are clustered into topics, and topics into 19 themes. fintopic.news is a front page built on the same API.

This package gives you two ways to use it:

• a Python client for scripts, notebooks and pipelines;
• an MCP server, so Claude, Cursor and other MCP clients can query the news directly.

You need a RapidAPI key that is subscribed to Finance Pulse. Create a RapidAPI account, open the plans page and subscribe to a plan: Basic is free and is enough to try everything below. Then copy your X-RapidAPI-Key from the API's page on RapidAPI (the endpoint playground shows it). A key that is not subscribed to a plan is answered with 403.

Use it from Claude or Cursor (MCP)

The server runs with uvx, which is part of uv: install uv first. The package itself needs no separate install.

Claude Code

claude mcp add --env RAPIDAPI_KEY=YOUR_RAPIDAPI_KEY --transport stdio finance-pulse -- uvx finance-pulse

Claude Desktop and Cursor

• Claude Desktop: Settings > Developer > Edit Config opens claude_desktop_config.json (macOS ~/Library/Application Support/Claude/, Windows %APPDATA%\Claude\). Quit and restart Claude Desktop completely afterwards.
• Cursor: ~/.cursor/mcp.json

{
  "mcpServers": {
    "finance-pulse": {
      "command": "uvx",
      "args": ["finance-pulse"],
      "env": { "RAPIDAPI_KEY": "YOUR_RAPIDAPI_KEY" }
    }
  }
}

If the server does not start in Claude Desktop, it usually cannot find uvx: put the full path (the output of which uvx, or where uvx on Windows) in "command".

uvx keeps the version it installed first. To move to a new release, use finance-pulse@latest in place of finance-pulse once, or run uv cache clean finance-pulse.

Then ask things like:

• "What is the news saying about Nvidia today? Only the key facts, with sources."
• "Which tickers are suddenly getting more coverage than yesterday?"
• "What are the top developing stories in Energy, and how did the biggest one grow?"
• "Show me Tesla's daily news sentiment for the last two weeks."

Tools

Tool	What it does
search_statements	Statements by symbol, sector, theme, topic, sentiment, minimum importance and time range
trending	The developments most outlets are reporting now, or per 2-hour slot over 7 days
find_topics	Developments for a symbol, sector or theme, by velocity, size or recency
get_topic	One development: counts, its mix of news, analysis and reddit statements, newest statements with their outlets
sentiment_series	Daily or hourly news volume and sentiment for a symbol, sector, theme or topic
screen	Symbols (optionally only equities and ETFs) or sectors ranked by news attention and its change against the previous period
themes	The standing subjects with their volume and sentiment, optionally only those a symbol or sector appears in
reference	The data window, known data incidents, theme ids and accepted filter values

Each tool call is one API request. Results are trimmed and default to small pages, so the free plan goes a long way.

Use it from Python

pip install finance-pulse
export RAPIDAPI_KEY=YOUR_RAPIDAPI_KEY

The MCP SDK is installed with the package even if you only use the client.

from datetime import datetime, timedelta, timezone

from finance_pulse import FinancePulse

fp = FinancePulse()          # or FinancePulse("YOUR_RAPIDAPI_KEY")

# The newest key facts about a ticker
for s in fp.statements(symbol="NVDA", importance_min="high", limit=5)["data"]:
    print(s["published_at"][:16], s["sentiment"], s["statement"], f"({s['source_domain']})")

# Which tickers are suddenly in the news (kind= leaves out rates, central banks, countries, ...)
for row in fp.symbols(period="d", sort="change", kind=["equity", "etf"], limit=10)["data"]:
    print(row["id"], row["mentions"], row["change"])

# Daily news volume and sentiment, last two weeks
since = (datetime.now(timezone.utc) - timedelta(days=14)).strftime("%Y-%m-%dT00:00:00Z")
for b in fp.series(symbol="TSLA", interval="day", since=since)["data"]["buckets"]:
    print(b["t"][:10], b["count"], b["score"])

# What most outlets are reporting right now
for t in fp.trending(kind="live")["data"][:10]:
    print(t["sources"], "sources:", t["name"])

Every method returns the API's JSON unchanged: {"data": ..., "next_cursor": ..., "snapshot": {...}}. The snapshot block says how fresh the data is.

Follow the news without missing anything

poll_feed yields every new statement in the order it entered the API and keeps its position in a file, so it survives restarts: after a crash nothing is skipped and only the statement you were working on is delivered again. The first run, with no saved position, starts 24 hours back, and the position is first stored once that first page has been handled. After a long pause the poller reads the whole backlog since its saved position, one request per 100 statements; delete the cursor file to start 24 hours back instead.

for s in fp.poll_feed(sector="Energy", importance_min="high", cursor_file="energy.cursor.json"):
    print(s["indexed_at"], s["statement"], s["source_url"])

The API builds new data about every 2.5 minutes, so the poller waits 150 seconds between polls by default. One poller at that rate makes about 576 requests a day. Timeouts and server errors are retried; a used-up quota (429) is raised.

More than one page

rows = list(fp.iter_statements(symbol=["NVDA", "AMD"], importance_min="medium", max_items=1000))

Each page of 100 is one request. Without max_items it stops after 500 rows; max_items=None reads to the end of the window.

Errors

from finance_pulse import FinancePulseError

try:
    fp.statements(symbol="S&P")
except FinancePulseError as e:
    print(e.status, e.title, e.detail, e.param)
    # 400 | Invalid symbol | 'S&P' is not a ticker | symbol

A 429 means the plan's request quota or rate limit is used up. Timeouts and network failures are raised as FinancePulseError too, with status 0; e.transient is true for those and for 5xx answers.

Methods

Method	Endpoint
statements, iter_statements, statement	/v2/statements, /v2/statements/{id}
feed, poll_feed	/v2/feed
series	/v2/series
symbols, sectors	/v2/symbols, /v2/sectors
trending	/v2/trending
topics, topic	/v2/topics, /v2/topics/{id}
themes, theme	/v2/themes, /v2/themes/{id}
meta	/v2/meta

The package targets Finance Pulse API 2.0 (the /v2 endpoints). Parameters and response fields are documented in the API reference, and the guides show complete worked examples.

What the data is, and is not

• Symbols are codes extracted by a language model, not exchange tickers. Most equities match their ticker; macro entities are codes too (FED, ECB, CRUDE, US10Y). Map the codes you trade before relying on them.
• Sentiment describes the statement, not a price forecast. A symbol's sentiment is the count of its statements' labels.
• The window is a rolling 62 days. meta() lists known outages and delays under incidents.

See the docs for the full list of caveats.

Development

pip install -e ".[dev]"
pytest

The tests use canned responses and make no API requests.

Releasing

1. Set the new version in src/finance_pulse/__init__.py and in server.json (two places).
2. Build and upload to PyPI (uv build && uv publish). From GitHub Actions, use pypa/gh-action-pypi-publish v1.14.2 or newer: older versions reject the metadata version this build writes.
3. Only then run mcp-publisher publish: the registry verifies the package on PyPI (it looks for the mcp-name line in its description), so the release must be there first.

Licence

MIT