feat(paper): expand on key features and comparisons to existing tools

Author: stefanoamorelli

paper.md

@@ -41,6 +41,18 @@ Key features that distinguish this software include:
 
 The software serves financial researchers, quantitative analysts, academic researchers, and developers who need programmatic access to financial data through conversational interfaces. By lowering the technical barrier to financial data access, it democratizes sophisticated financial analysis capabilities.
 
+## Comparison with existing tools
+
+Several Python libraries provide access to financial data, but each has limitations that this MCP server addresses:
+
+- **pandas-datareader**: Limited to basic financial data sources, lacks conversational interface
+- **yfinance**: Yahoo Finance focused, no natural language support, limited data coverage
+- **quandl/nasdaq-data-link**: Requires API knowledge and programming skills for basic queries
+- **Alpha Vantage API**: Limited free tier, requires manual API integration
+- **Bloomberg/Refinitiv terminals**: Expensive, proprietary, not accessible to most researchers
+
+The Nasdaq Data Link MCP Server uniquely combines comprehensive data coverage with zero-code access through natural language, making it the first tool to bridge professional-grade financial data with conversational AI interfaces [@python-financial-apis].
+
 # Implementation
 
 The server is implemented in Python using the official Model Context Protocol SDK [@mcp-python-sdk] and integrates with the Nasdaq Data Link Python SDK [@nasdaq-data-link-python]. The architecture follows a modular design with separate resource modules for each data source:
@@ -53,7 +65,7 @@ The server is implemented in Python using the official Model Context Protocol SD
 
 The server exposes 25+ tools through the MCP interface, each providing structured access to specific datasets. Error handling includes API rate limiting, data validation, and graceful fallbacks for missing data.
 
-Configuration is managed through environment variables and supports both development and production deployments. The software includes comprehensive logging and monitoring capabilities for production usage.
+Configuration is managed through environment variables and supports both development and production deployments. The software includes comprehensive logging and monitoring capabilities for production usage. The project was developed over approximately four months starting in March 2025, with over 115 commits demonstrating sustained development effort, continuous integration testing using pytest, and comprehensive documentation to ensure code quality and maintainability.
 
 # Usage Examples