"How to use Bootstrap framework" matching MCP servers:

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

An MCP server that edits files through a real Neovim instance, giving agents access to LSP diagnostics, formatting, code actions, and plugin commands.

Zero-dependency macOS desktop automation for AI agents. Screenshot, mouse, keyboard, clipboard, and window control via MCP. 18 tools, macOS 13+, one command: npx mac-use-mcp.

Provides native macOS computer control tools including mouse and keyboard simulation, screenshot capture, and application management for MCP-compatible agents. It enables AI assistants to directly interact with the macOS operating system and installed apps through standard tool calls.

MCP server for AI agents to send notifications, copy to clipboard, request confirmations, and collect text input from users across their devices.

Multi-framework MCP server that enables AI to generate clean, semantic CSS class names (e.g., btn-primary) instead of verbose utility strings, with support for Tailwind, Bootstrap, UnoCSS, Tachyons, SSR safety, and minification for token savings.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

MCP server that recommends which npm/PyPI libraries to use or avoid, with concise verdicts, alternatives, and code snippets, keeping responses under 500 tokens.

Extracts SVG icons from Figma and generates React TSX icon components automatically, enabling AI assistants like GitHub Copilot and Claude to manage icon workflows.

Provides tools for AI agents to search, browse, and retrieve the full documentation for the mcp-framework. It enables agents to access documentation sections and page content directly within MCP-compatible environments like Claude Code and Cursor.

The Excel to JSON MCP provides a standardized interface for converting Excel and CSV data into JSON format using the Model Context Protocol.

A template project for building Model Context Protocol servers with FastMCP framework, Docker support, and example CRUD API implementation to help developers quickly bootstrap their own MCP servers.

A general-purpose MCP server that provides utility tools for echo, date/time, and file operations within Claude Code and Claude Desktop. It functions as an extensible framework designed to help developers easily build and register custom Python-based tools.

The JSON to Excel MCP provides a standardized interface for converting (1)JSON data (2)URL pointing to JSON files into CSV format

Provides AI knowledge base query service for Cocos Creator 3.8.8 + exia-framework, enabling semantic search and API lookup for framework and engine classes.

An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.

Converts REST API code (like NestJS controllers or FastAPI endpoints) to Postman collections and environments, helping developers automatically sync their API endpoints with Postman.

An MCP server that converts Markdown files containing Mermaid diagrams into PDF documents by rendering diagrams as SVG images. It provides a specialized tool to automate document conversion while ensuring all visual charts are correctly embedded in the final output.

Converts JSON data to TOON (Token-Oriented Object Notation) format and back, reducing token usage by 30-60% for more efficient LLM applications.