"Performing Email-Related Operations in Python" matching MCP servers:

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

A Python-based MCP server that enables users to interact with the esa.io API for documentation management. It provides tools for retrieving user information and performing full CRUD operations on articles.

Exposes a small catalog of programming study topics through the Model Context Protocol (MCP) for local learning.

Python Docs MCP Server gives AI coding agents local access to the official Python standard library docs. It can look up docs by Python version, return specific sections, and keep answers compact for clients like Claude Desktop, Cursor, and Codex. It runs without API keys or a hosted service.

Enables Claude or any MCP client to drive a live SOLIDWORKS session through its COM API, modeling parts, building assemblies with mates, checking interferences and machining feasibility, and exporting STEP or DXF files. It also exposes a local semantic index of the SOLIDWORKS API so the model can look up signatures, enums, and official examples before running arbitrary Python against the session.

Provides up-to-date Python package manager commands by cross-referencing official pip, poetry, uv, and conda documentation with automatic weekly updates.

An MCP server that enables users to fetch Python documentation using the Brave Search API through natural language queries.

Provides real-time access to code library documentation and examples through Context7 API integration, enabling AI assistants to retrieve up-to-date technical documentation, code snippets, and best practices for various programming libraries.

Exposes the structure and documentation of the openreview-py library, enabling LLMs to discover classes, functions, and signatures for accurate code generation. It provides tools for searching the API and retrieving detailed metadata without executing code or making external API calls.

An MCP (Model Context Protocol) server that turns the book Deep Learning with Python (François Chollet) into a searchable knowledge base, so Claude can act as a deep-learning/ML expert grounded in the book's content.

Rust-powered PDF toolkit over MCP: create, read, and analyze PDFs; extract text and entities for RAG; convert to Markdown; split/merge/rotate/reorder pages; manage form fields and annotations; encrypt documents. Runs locally via uvx oxidize-mcp.

Enables interaction with Zhejiang University's learning platform (学在浙大 / 智云课堂) via MCP tools, allowing natural language commands to check todos, view schedules, fetch lecture transcripts, and submit homework.

An MCP server that serves documentation and enables AI-powered search, Q\&A, and document analysis for developer tools and guides.

Enables retrieval and cleaning of official documentation content for popular AI/Python libraries (uv, langchain, openai, llama-index) through web scraping and LLM-powered content extraction. Uses Serper API for search and Groq API to clean HTML into readable text with source attribution.

A server that connects Claude to your documentation via Inkeep's API, enabling AI-powered interactions with your documentation content.

Enables AI assistants to search documentation of packages and services to find implementation details, examples, and specifications.

Provides LLMs with comprehensive knowledge about the V programming language, including documentation access, code examples, standard library reference, and syntax explanations to help users learn and work with V.

A read-only reference server for 303 curated generative art algorithms implemented in Python (py5), spanning physics, fractals, cellular automata, shaders, and more. Agents can search by keyword, visual mood (ethereal, chaotic, crystalline…), or multi-layer artistic intent to discover algorithms, read structured summaries, and fetch bounded source snippets.

Provides AI assistants with direct access to Jamf official documentation, enabling them to answer Jamf-related questions by searching, retrieving articles, and browsing product documentation.

Enables users to access, search, and get recommendations from AWS documentation through natural language queries. Supports both global AWS documentation and AWS China documentation with tools to fetch pages, search content, and discover related resources.