punkpeye/awesome-mcp-servers
Mar 2026 – Mar 2026
1 Month active
Languages Used
Markdown
Technical Skills
AI integrationdocumentationproject managementreadability improvementtrading systems
michaeljiangmingfeng-debug
PROFILE
Michaeljiangmingfeng-debug
Michael Jiang contributed to the punkpeye/awesome-mcp-servers repository by integrating the QuantToGo MCP server, which delivers macro-factor quantitative trading signals for AI agents and broadens the repository’s trading system capabilities. He focused on enhancing documentation and onboarding by refining the README for improved readability and discoverability, standardizing emoji formatting, and adding relevant links for QuantToGo users. His technical approach emphasized clear commit hygiene and consistent documentation practices, leveraging Markdown and project management skills. While no bugs were addressed during this period, Michael’s work deepened the repository’s AI integration and usability, supporting both developer experience and the expansion of trading tools.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
5
Activity Months1
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Work History
March 2026
3 Commits • 2 Features
Mar 1, 2026March 2026 performance summary for punkpeye/awesome-mcp-servers. Key deliveries include a QuantToGo MCP server integration and README enhancements to improve readability and discoverability. No major bugs fixed this month. Overall impact: expanded quantitative trading tooling for AI agents, improved onboarding and visibility for QuantToGo users, and stronger documentation quality. Technologies demonstrated: MCP server integration, commit hygiene, README/documentation engineering, and UX-focused improvements.
3 Commits • 2 Features
Mar 1, 2026March 2026 performance summary for punkpeye/awesome-mcp-servers. Key deliveries include a QuantToGo MCP server integration and README enhancements to improve readability and discoverability. No major bugs fixed this month. Overall impact: expanded quantitative trading tooling for AI agents, improved onboarding and visibility for QuantToGo users, and stronger documentation quality. Technologies demonstrated: MCP server integration, commit hygiene, README/documentation engineering, and UX-focused improvements.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage46.6%
Skills & Technologies
Programming Languages
Markdown
Technical Skills
AI integrationdocumentationproject managementreadability improvementtrading systems
Repositories Contributed To
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