Discover and integrate MCP servers tagged with system
19 system MCP Servers Available
Node.js/TypeScript MCP server for Atlassian Bitbucket. Enables AI systems (LLMs) to interact with workspaces, repositories, and pull requests via tools (list, get, comment, search). Connects AI directly to version control workflows through the standard MCP interface.
Node.js/TypeScript MCP server for Atlassian Confluence. Provides tools enabling AI systems (LLMs) to list/get spaces & pages (content formatted as Markdown) and search via CQL. Connects AI seamlessly to Confluence knowledge bases using the standard MCP interface.
A long-term memory storage system for LLMs using the Model Context Protocol (MCP) standard. This system helps LLMs remember the context of work done over the entire history of a project, even across multiple sessions. It uses semantic search with embeddings to provide relevant context from past interactions and development decisions.
Node.js Model Context Protocol (MCP) server providing secure, relative filesystem access for AI agents like Cline/Claude.
🗂️ A Model Context Protocol (MCP) server that provides integration with Turso databases for LLMs. This server implements a two-level authentication system to handle both organization-level and database-level operations, making it easy to manage and query Turso databases directly from LLMs.
Memento MCP: A Knowledge Graph Memory System for LLMs
PromptLab transforms basic user queries into optimized prompts for AI systems --> Built using MCP
A secure Model Context Protocol (MCP) server providing filesystem access within predefined directories
An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.
Connect Claude to Linear project management systems. Retrieve, create, and manage issues and projects seamlessly.
Secure middleware server implementing Model Context Protocol (MCP) over SSE with JWT authentication. Enables standardized communication between AI tools and clients with dynamic tool registration, request logging, and session management. Perfect for building production-ready AI systems requiring secure access patterns.
Powerful Model Context Protocol (MCP) implementation for visualizing directory structures with real-time updates, configurable depth, and smart exclusions for efficient project navigation
A Model Context Protocol (MCP) server that provides filesystem operations for Claude AI
The Gatherings MCP Server provides an API that allows AI assistants to interact with the Gatherings application through the Machine Conversation Protocol. This enables AI systems to help users manage shared expenses for social events, outings, or any gathering where costs are split among participants.
A Model Context Protocol (MCP) server for Apache Dolphinscheduler. This provides access to your Apache Dolphinshcheduler RESTful API V1 instance and the surrounding ecosystem.
MCP server that enables communication and coordination between different Roo modes/roles across multiple systems
Go server implementing Model Context Protocol (MCP) for filesystem operations.
A server that integrates Linear's project management system with the Model Context Protocol (MCP) to allow LLMs to interact with Linear.
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.
MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.
MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.
Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.
