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Why this server?
Enables AI assistants like Claude to perform Python development tasks through file operations, code analysis, project management, and safe code execution, essential for developing ML pipelines.
Why this server?
Enables interaction with Jupyter notebooks through the Model Context Protocol, supporting code execution and markdown insertion within JupyterLab environments, which is beneficial for prototyping and documenting ML pipelines.
Why this server?
An MCP server that wraps the Riza Code Interpreter API and presents endpoints as individual tools, enabling execution of Python code necessary for ML pipeline development.
Why this server?
A server that enables interaction with PostgreSQL, MySQL, MariaDB, or SQLite databases through Claude Desktop using natural language queries, useful for data handling in ML pipelines.
Why this server?
A simple template for creating custom tools for Cursor IDE using Model Context Protocol, deployable via Heroku, Docker, or directly within Cursor IDE, enabling the user to create their own tools for ML development
Why this server?
This project aims to build a Claude Code MCP server and implement its associated tools (explain_code, review_code, fix_code, edit_code, test_code, simulate_command, your_own_query). The server is implemented using Node.js and the MCP SDK. It receives tool requests from clients via Stdio, dynamically allowing users to work on their ML Code.
Why this server?
A Model Context Protocol server that provides code analysis capabilities using tree-sitter, designed to give Claude intelligent access to codebases with appropriate context management. This would help with building and analysing ML projects.
Why this server?
Provides AI-powered assistance for coding problems using Google's Gemini AI, combined with Perplexity insights and Stack Overflow references, facilitating contextual analysis and automatic response archiving for improved troubleshooting in ML pipelines.
Why this server?
Enables browser automation using Python scripts, offering operations like taking webpage screenshots, retrieving HTML content, and executing JavaScript. These might be useful for building dataset ingestion pipelines.
Why this server?
Provides tools for accessing coding style guidelines and best practices for Python, helping to maintain high quality in the developed ML Pipelines.