Use Finance Pulse from Claude, Cursor or Python

The finance-pulse package wraps the API twice: as a Python client for scripts, notebooks and pipelines, and as an MCP server that lets an AI assistant ask the news questions itself. Its source is on GitHub, and the server is listed in the official MCP registry as io.github.SlothyAfk/finance-pulse.

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

Shell

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

Claude Desktop and Cursor. In 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. In Cursor, the file is ~/.cursor/mcp.json.

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".

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 ongoing stories about Microsoft, and how did the biggest one unfold?"
"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."

The assistant picks from these tools (the package's README lists all of them):

Tool	What it does
`get_symbol`	Everything about one company or other entity in one call: counts and sentiment per period, largest topics, storylines, related entities, newest statements
`find_symbol`	A company's code from its name ("micron" finds `MU`)
`search_statements`	Statements by symbol, sector, theme, topic, storyline, sentiment, minimum importance and time range
`trending`	The developments most outlets are reporting right now, or the top of each recent time slot
`find_topics`	Developments for a symbol, sector, theme or storyline, by velocity, size or recency
`get_topic`	One development: counts, its mix of news, analysis and reddit statements, its storylines, newest statements with their outlets
`storylines`	One company's news grouped into continuing stories: an event, then its follow-ups and reactions
`get_storyline`	One storyline: its topics in order and its newest statements
`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, for the last day, week or any interval
`themes`	The standing subjects with their volume and sentiment, optionally only those a symbol or sector appears in
`reference`	How far back your plan's history goes, counts, theme ids and accepted filter values

Each tool call is normally one API request. Results are trimmed and come in small pages.

From Python

Shell

pip install finance-pulse
export RAPIDAPI_KEY=YOUR_RAPIDAPI_KEY

Python

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 reference documents every endpoint.

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 at the feed's default starting point (a limited time back, not the beginning of the data).

Python

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 poller pauses 150 seconds between polls unless you pass interval=. Every poll is one request, so at the default a poller that runs all month makes about 17,000 requests: set interval= to fit your plan's monthly quota. Timeouts, server errors and a passing 429 are retried; a used-up quota is raised at once. The feed guide explains what the cursor guarantees and why the log it builds is your own point-in-time history.

Run this yourself: get an API key on RapidAPI ↗.