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Enables AI assistants to manage Docker containers, execute commands inside them, and inspect container information through a minimalist set of tools supporting both stdio and HTTP transports.

Container Use

MCP server to add container superpowers to your AI agent

Enables AI agents to manage Contentrain CMS content, models, and assets with automatic git branch synchronization across different environments. It provides standardized tools for performing CRUD operations on git-based headless CMS projects through natural language.

Contentstack MCP Server

An MCP Server for Contentstack.

Context7 MCP is a service that provides developers with the latest code documentation and examples. By integrating into the development environment, it ensures that the code generated by LLMS is based on the latest library documentation.

Provides LLMs with up-to-date, version-specific documentation and code examples directly from library sources, eliminating outdated training data and hallucinated APIs by fetching current documentation at prompt time.

Provides up-to-date, version-specific documentation and code examples for software libraries directly to LLMs, enabling resolution of library identifiers and retrieval of relevant documentation with code snippets.

Provides LLMs with up-to-date, version-specific documentation and code examples from library sources directly into prompts, eliminating outdated code generation and hallucinated APIs.

Provides up-to-date, version-specific documentation and code examples for libraries and frameworks directly into AI prompts, eliminating outdated code generation and hallucinated APIs.

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.

Provides access to the Context7 API for searching up-to-date documentation, code examples, API references, and troubleshooting help across thousands of programming libraries and frameworks. Enables developers and AI agents to quickly find accurate documentation, compare libraries, get migration guides, and resolve coding issues.

Context Engine

Virtual AI-centric IDE for LLMs and AI Agents

Provides real-time access to up-to-date library documentation and code examples for any programming library. Helps AI coding assistants deliver accurate, current information instead of relying on outdated training data.

Enables semantic code search and AI-powered Q\&A over vectorized repositories directly within Claude. It provides tools to search code chunks, answer questions grounded in source code, and inspect repository snapshot metadata.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Context-Pods is a comprehensive development framework for creating, testing, and managing Model Context Protocol (MCP) servers. It provides a Meta-MCP Server that can generate other MCP servers through natural language descriptions or by wrapping existing scripts.

Provides AI assistants with persistent memory and code intelligence across all tools and conversations. Features semantic search, knowledge graphs, decision tracking, and impact analysis with 60+ tools for universal context preservation.

An open-source memory layer that provides persistent project context and architectural history for AI development tools across multiple platforms and sessions. It enables AI assistants to maintain a shared understanding of codebases while integrating directly with services like Notion for documentation management.

Continuity Server Fixed

Versão corrigida do MCP Continuity Server compatível com SDK 1.7.0

Automatically extracts architectural decisions, patterns, and insights from Git commits to build a local, structured project memory. It exposes this living context to AI tools via MCP, allowing them to understand the historical reasoning and evolution behind your codebase.