punkpeye/awesome-mcp-servers
Mar 2026 – Mar 2026
1 Month active
Languages Used
Markdown
Technical Skills
MATLABPythondocumentationfull stack developmentproject management
Hannes Suhr
PROFILE
Hannes Suhr
Sannah Rush developed the core MATLAB MCP Server for the punkpeye/awesome-mcp-servers repository, enabling AI agents to execute MATLAB code, manage asynchronous jobs, and generate interactive plots. She integrated a Python-based execution path to streamline MATLAB code execution within existing AI tooling, enhancing interoperability and workflow efficiency. Sannah also introduced a user-facing Glama score badge to provide visibility and performance metrics, updating documentation in Markdown to clarify new capabilities and guide adoption. Her work demonstrated depth in full stack development, MATLAB, and Python, focusing on robust integration and clear user guidance rather than breadth, with no bug fixes reported this period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
3
Activity Months1
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Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 focused on delivering a core MATLAB MCP Server to the punkpeye/awesome-mcp-servers repository, enabling AI agents to execute MATLAB code, run asynchronous jobs, and generate interactive plots. The month also introduced a user-facing visibility metric via a Glama score badge and updated documentation to reflect new capabilities and performance insights. A Python-based MATLAB execution path was added to the Code Execution flow to streamline integration with existing AI tooling.
2 Commits • 1 Features
Mar 1, 2026March 2026 focused on delivering a core MATLAB MCP Server to the punkpeye/awesome-mcp-servers repository, enabling AI agents to execute MATLAB code, run asynchronous jobs, and generate interactive plots. The month also introduced a user-facing visibility metric via a Glama score badge and updated documentation to reflect new capabilities and performance insights. A Python-based MATLAB execution path was added to the Code Execution flow to streamline integration with existing AI tooling.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Markdown
Technical Skills
MATLABPythondocumentationfull stack developmentproject management
Repositories Contributed To
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