"A Python library for visualizing neural networks" matching MCP servers:

A Python MCP server that generates complete Vue 3 CRUD structures with Composition API, including components, services, stores, and routes.

A Model Context Protocol server that allows LLMs to interact with Python environments, execute code, and manage files within a specified working directory.

Enables LLMs to run Python code and scripts across system and conda environments, list and install packages, and read or write text and binary files within an isolated working directory. It also provides prompt templates for generating, refactoring, and debugging Python code.

Enables LLMs to interact with Python environments, run code and scripts, manage packages, and read or write files within a configured working directory.

Enables LLMs to manage Python environments and packages, execute Python code or scripts, and read or write files within a secured working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to interact with Python environments, execute code, manage packages, and perform file operations within a configurable working directory.

Enables LLMs to run Python code and scripts across available environments, manage packages, and read/write files within a secured working directory. It also provides prompts and resources for common Python development tasks.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes environment, package, and file resources plus prompt templates for creating, refactoring, and debugging Python code.

An MCP server that enables searching for specific files by name within the current directory and its subdirectories. It uses Server-Sent Events (SSE) to provide a find_file tool for locating local files and libraries.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

An MCP server that exposes macOS Spotlight search as tools, wrapping mdfind and mdls to provide full-text and metadata file search across the system.

Enables LLMs to interact with Python environments, execute code, manage files, and handle packages through the Model Context Protocol.

A secure, read-only MCP server for browsing and searching files in a specified directory with path traversal protection and .gitignore support.

A full-featured secure MCP server for local file system operations, with built-in image processing, OCR and media tools. Fully compliant with the official Model Context Protocol specification, offering standardized request/response schemas, large-file streaming I/O, multi-transport remote deployment, and comprehensive text search & replace functionality for LLM agent integration.

Enterprise-grade MCP server integrating Claude AI capabilities, enabling AI-powered conversations, file operations, and system tools via the Model Context Protocol.

A native Nextcloud app that exposes the instance as an MCP server so Claude and other MCP clients can read, search, and (with admin-granted permissions) edit files, notes, calendars, Deck cards, and Talk conversations. It runs inside Nextcloud with OAuth or app-password sign-in, applying each user's own Nextcloud permissions and admin-controlled per-operation grants.

A collection of small MCP servers for macOS automation that expose Apple Mail, MoneyMoney, and Spotlight functionality as tools via AppleScript and command-line, enabling AI applications to search mail, manage contacts and transactions, and perform system-wide file searches through natural language.

A secure MCP server enabling read-only access and file search capabilities within a specified directory, while respecting .gitignore patterns.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read or write files within an isolated working directory.

Enables LLMs to interact with local Python environments, execute code and scripts, manage packages, and read or write files within an isolated working directory.