punkpeye/awesome-mcp-servers
Mar 2026 – Mar 2026
1 Month active
Languages Used
Markdown
Technical Skills
AI IntegrationAPI DevelopmentAPI integrationJavaScriptdocumentation
David Landais
PROFILE
David Landais
David Landais developed an OVH API MCP integration for the punkpeye/awesome-mcp-servers repository, enabling large language models to access OVH endpoints through a streamlined MCP server interface. He focused on AI integration and API development, using JavaScript and Markdown to implement the feature and enhance project documentation. By updating the README and linking the ovh-api-mcp project, David improved onboarding and discoverability for developers integrating OVH APIs with LLMs. His work emphasized production readiness, clear commit messages, and maintainability, though the scope was limited to a single feature over one month without major bug fixes, reflecting a focused and well-documented contribution.
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: Delivered OVH API MCP integration for LLM access in punkpeye/awesome-mcp-servers, enabling LLMs to access OVH endpoints via a simplified MCP server interface. Updated documentation to improve onboarding and visibility by linking the ovh-api-mcp project. No major bugs fixed this month; focus was on delivering a production-ready feature and improving developer experience through README enhancements and commit-quality messages.
2 Commits • 1 Features
Mar 1, 2026March 2026: Delivered OVH API MCP integration for LLM access in punkpeye/awesome-mcp-servers, enabling LLMs to access OVH endpoints via a simplified MCP server interface. Updated documentation to improve onboarding and visibility by linking the ovh-api-mcp project. No major bugs fixed this month; focus was on delivering a production-ready feature and improving developer experience through README enhancements and commit-quality messages.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Markdown
Technical Skills
AI IntegrationAPI DevelopmentAPI integrationJavaScriptdocumentation
Repositories Contributed To
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