finviz-screener-mcp

finviz-screener-mcp

MCP server exposing Finviz's US stock screener via finvizfinance, allowing filter-based screening and custom table columns as structured JSON.

Category
Visit Server

README

finviz-screener-mcp

FastMCP server that exposes only Finviz’s stock screener via finvizfinance.

US-only: every screen() call hardcodes Country=USA. No news, maps, insider trading, or Elite API — just screener filters → structured JSON.

Tools

Tool Purpose
list_filters Progressive discovery (index / one category / one section / search) — avoids dumping ~1694 values
screen Compact screener results (default limit 25, max 100; offset capped at 400; null fields and the always-USA Country column stripped)

Both tools are annotated read-only (readOnlyHint) so clients can skip confirmation prompts. Invalid input and upstream failures are raised as MCP tool errors (isError: true) with messages like [invalid_filter] ... or [upstream] ..., so agents can self-correct.

Keep agent context small

list_filters()                         → category names + counts only
list_filters(category="Sector")        → values for Sector only
list_filters(section="signals")        → signals only
list_filters(query="rsi")              → matching names
screen(..., limit=15, table="custom", columns=[...])  → only needed fields

Example screen call:

{
  "filters": {
    "Sector": "Technology",
    "Market Cap.": "Large ($10bln to $200bln)"
  },
  "table": "custom",
  "order": "Market Cap.",
  "ascend": false,
  "columns": ["Ticker", "Market Cap.", "P/E", "RSI", "Price"],
  "limit": 15
}

Important caveats

  • Unofficial scrape. finvizfinance parses the free Finviz HTML screener. There is no free official API; Elite’s CSV/API export is a separate paid surface and is not used here.
  • Quotes on the free site are delayed.
  • Scraping can break if Finviz changes its DOM, and may be against Finviz Terms of Service. Use responsibly.

Local setup

Requires Python 3.11+ (3.12 recommended; see .python-version).

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt

Run the server (stdio):

fastmcp run main.py:mcp
# or
python main.py

Inspect tools:

fastmcp inspect main.py:mcp

Cursor / Claude Desktop (stdio)

{
  "mcpServers": {
    "finviz-screener": {
      "command": "uv",
      "args": ["run", "--with-requirements", "requirements.txt", "fastmcp", "run", "main.py:mcp"],
      "cwd": "/absolute/path/to/finviz-screener-mcp"
    }
  }
}

Or with an activated venv:

{
  "mcpServers": {
    "finviz-screener": {
      "command": "/absolute/path/to/finviz-screener-mcp/.venv/bin/fastmcp",
      "args": ["run", "main.py:mcp"],
      "cwd": "/absolute/path/to/finviz-screener-mcp"
    }
  }
}

No API key or .env is required.

Deploy on Prefect Horizon (FastMCP Cloud)

Horizon is the managed host from the FastMCP team (docs; console: horizon.prefect.io). URLs look like https://{name}.fastmcp.app/mcp.

  1. Push this repo to GitHub.
  2. Sign in at horizon.prefect.io with GitHub and select the repo.
  3. Configure:
    • Entrypoint: main.py:mcp
    • Dependencies: requirements.txt (auto-detected)
  4. Deploy. Horizon ignores if __name__ == "__main__" and installs deps from requirements.txt.
  5. Connect clients to https://your-server-name.fastmcp.app/mcp.

No environment variables or secrets are required for this server. If you add any later: Settings → Environment Variables, mark sensitive, then rebuild and redeploy (env changes do not hot-reload).

Smoke check

python -c "
from main import list_filters, screen
print([c['name'] for c in list_filters()['categories'][:5]])
print(screen(filters={'Sector': 'Technology'}, limit=3)['count'])
"

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured