mcp gitlab

Domain-aware memory persistence via a graph store with multi-backend options and visualizations for AI context management.

Claude-driven Strava integration delivering AI insights, route planning, and rich activity analytics.

Cross-platform screenshot tool delivering web page captures and native OS screenshots with secure, MCP-compatible integration.

Lightweight, stateful orchestration with TOON compression and lazy-loading to maximize throughput and minimize token usage.

Unified secure identity, DeFi-powered payments, and audited service marketplace enabling open-agent interactions.

Documentation

Inspectr – easily view and analyze API requests, MCP server calls and webhook events.

Open-source AI agent orchestration powered by LangGraph and MCP, enabling self-hosted, scalable agent coordination with enterprise-ready deployment options for teams.

This is a MCP designed to manage and interact with mobile devices and simulators.

A curated collection of persona-based mcp server & tool groupings.

emacs mcp server

MCP Crew AI Server is a lightweight Python-based server designed to run, manage and create CrewAI workflows.

A light wrapper around mcp-swift-sdk for easy usage of MCP clients in Swift

MCP Server with Remote SSH support

Claude MCP Server to work with figma

mcp_services_radarr_sonarr

Automatic browser-auth MCP tooling with local credentials for cross-site access.

A lightweight, real-time file context provider with advanced code metrics, caching, and context-aware search for LLM workflows.

AI-driven note management with DynamoDB storage, dual interfaces, model-function calls, and project-independent personal notes.

Ontology-driven MCP server for graph-aware Text-to-SQL with OBQC validation across multiple databases.

Java MCP.Run client enabling installation management and tool invocation with a thread-safe cache and flexible configuration.

Hybrid Neo4j-Qdrant MCP server enabling context-augmented reasoning, dynamic knowledge extraction, and citation-aware workflows for coding and research.

Natural language bridge to Kubernetes, enabling intuitive control over pods, deployments, services, and cluster operations with LLMs.