MCP Securities Analysis

A Python-based flow for securities analysis using the Model Context Protocol (MCP). The repository bundles data-collection, parsing, analytics and visualisation tools behind a single FastMCP server so that they can be consumed locally or remotely by any MCP-aware client (e.g. Claude Desktop, LangChain, OpenAI-Function calling, etc.).

Example deep research report for Tesla.

This was generated semi-autonomously by the following steps:

connect MCP tools to Claude Desktop, including web search, Perplexity, Wikipedia, in addition to the market data tools in server.py for fundamental, technical analysis, and news search.
prompt Claude Desktop to query Perplexity, Wikipedia, and the 10-K to write a profile of Tesla
prompt Claude Desktop to query each tool for info on Tesla
finally, enable deep research and prompt Claude Desktop to write a deep report in 8 sections with details on what each section should cover, using the information retrieved from the tools.

While it's not a fully autonomous agent and at an early POC level, it shows clear path toward a fully autonomous agent. Create an MCP client that goes through the steps above and generates a deep report on Tesla in a structured format with graphs and tables. And then create an even more advanced multi-agent workflow with a set of parallel agents for each section, and a critic-optimizer workflow, and a final report generator.

Features

FastMCP server – exposes a few MCP tools to get market data, news, charts, SEC filings, fundamental, technical data, research from public web sites, subscription services, and REST APIs.
Market data – real-time and historical OHLCV data via yfinance & OpenBB.
Fundamental data – automatic downloading of SEC filings (sec_downloader) and rich XBRL/HTML parsing through sec_parser.
News & Social sentiment – headlines with newsapi-python plus Reddit scraping utilities.
Technical analysis – hundreds of indicators with pandas_ta & TA-Lib.
Interactive plots – high-quality Plotly charts exported server-side (static PNG or interactive HTML).
Async-first design – built on asyncio, aiohttp, httpx & Playwright for maximum throughput.

this section AI-generated so beware of hype. New project, would like to share and get comments, not extensively tested. Use it as a starting point, at your own risk.

Quick Start

# 1. Clone and enter the project
$ git clone https://github.com/<your-org>/MCP.git
$ cd MCP

# 2. Create & activate a virtualenv (recommended)
$ python -m venv .venv
$ source .venv/bin/activate

# 3. Install python dependencies
$ pip install -r requirements.txt

# 4. Install Playwright browsers (once)
$ playwright install

# 5. Copy environment template & add your keys
$ cp dotenv.txt .env  # then edit as needed

# 6. Launch and test the server
$ LOGLEVEL=DEBUG mcp dev server.py
# click to the link in the terminal to open the test page, connect, view tools, and then test them individually

# 7. Use the server in your MCP client of choice. For Claude Desktop, install the provided claude_desktop_config.json file for your platform (macOS, Windows).

Project Structure

MCP/
├── claude_desktop_config.json # Configuration for Claude Desktop
├── dotenv.txt             # Secrets / Environment variables
├── README.md              # This file
├── server.py              # FastMCP server, launched by mcp dev or Claude desktop or other MCP client
├── requirements.txt       # Python dependencies
├── sources.yaml           # Data-source configuration used by server
├── Market Data.ipynb      # Jupyter notebook to fetch market data
├── TearSheet.ipynb        # Jupyter notebook to do basic analysis

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A Python-based FastMCP server that provides financial tools for securities analysis, including market data, news, fundamental/technical analysis, and visualization capabilities that can be consumed by any MCP-aware client.

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