Docker AI Agent Skills

title: System for Cross-domain Identity Management (SCIM) MCP Server colorFrom: pink colorTo: blue sdk: docker appport: 7860 pinned: false shortdescription: SCIM 2.0 relay for AI agents to manage identities - building-mcp-track-enterprise scim-mcp is a SCIM (System for Cross-domain Identity Management) relay for AI agents. This MCP server enables LLMs to manage enterprise user identities and group

A comprehensive security operations platform that integrates multiple security tools into a unified interface. This platform provides a centralized way to run various security scanning and testing tools. - Unified Interface: Single entry point for multiple security tools - Docker Support: Easy deployment using Docker - JSON Output: Consistent JSON output format across all tools - Error Handling: R

A Model Context Protocol (MCP) server that enables Claude Desktop (and other MCP clients) to generate images, videos, music, and audio using Fal.ai models. - Native Async API - Uses falclient.runasync() for optimal performance - Queue Support - Long-running tasks (video/music) use queue API with progress updates - Non-blocking - All operations are truly asynchronous - STDIO - Traditional Model Con

A high-performance, pure Rust Model Context Protocol (MCP) server that provides persistent, semantic, and graph-based memory for AI agents. Works perfectly with: Claude Desktop Claude Code (CLI) Cline / Roo Code Any other MCP-compliant client. Unlike other memory solutions that require a complex stack (Python + Vector DB + Graph DB), this project is a single, self-contained executable. ✅ No Extern

Find emails needing follow-up -- unanswered sent emails, flagged unreplied, draft contextual responses

An MCP server implementation for retrieving information from Pinecone Assistant. - Retrieves information from Pinecone Assistant - Supports multiple results retrieval with a configurable number of results - Docker installed on your system - Pinecone API key - obtain from the Pinecone Console - Pinecone Assistant API host - after creating an Assistant (e.g. in Pinecone Console), you can find the ho

A Model Context Protocol (MCP) server that enables AI assistants like Claude to interact with your Obsidian vault. Access your notes, create content, manage tags, and search your knowledge base through natural conversation. - CRUD Operations: Create, read, update, and delete notes (with safety confirmation) - Write Modes: Overwrite, append, or prepend content - Batch Reading: Read multiple notes i

A Model Context Protocol (MCP) server that provides AI-powered PostgreSQL performance tuning capabilities. This server helps identify slow queries, recommend optimal indexes, analyze execution plans, and leverage HypoPG for hypothetical index testing. - Retrieve slow queries from pgstatstatements with detailed statistics - Analyze query execution plans with EXPLAIN and EXPLAIN ANALYZE - Identify p

English | 中文 Zero-dependency MCP memory server for AI agents — persistent, searchable, local-first, single binary. - No infra to babysit. Single Go binary. No Docker, no Node.js runtime, no cloud account, no API keys. brew install in 30 seconds. - Memory stays with the project. Stored in .aimemo/ next to your code — commit it to git or add it to .gitignore. Switch branches; memory follows the dire

An MCP (Model Context Protocol) server that provides token-efficient access to OpenAPI (v3.0) and Swagger (v2.0) specifications via MCP Resource Templates. The primary goal of this project is to allow MCP clients (like Cline or Claude Desktop) to explore the structure and details of large OpenAPI specifications without needing to load the entire file into an LLM's context window. It achieves this

Give your agents memory that survives the session, so they stop re-deriving what they already learned and stop repeating past mistakes. Hybrid recall (BM25 + dense + RRF), bi-temporal history, and AES-256-GCM at rest, exposed as 55+ MCP tools that work with any host. 73.8% on LongMemEval's official harness (vs Zep 63.8%, Mem0 49.0%). One binary. One file. No Docker. No Postgres. No cloud. Local-fi

AI-powered debugging and inspection for Kubernetes clusters using Inspektor Gadget. - AI-powered interface for Kubernetes troubleshooting and monitoring - One-click Inspektor Gadget deployment and removal - Intelligent output summarization and analysis - Automatic gadget discovery from Artifact Hub 1. Ensure you have Docker and a valid kubeconfig file 2. Configure the MCP server in VS Code (see IN

You can run the MCP Server in a Docker container. This is useful if you want to avoid managing Python environments or dependencies on your local machine. See kestramcpdocker. Paste the following configuration into your MCP settings (e.g., Cursor, Claude, or VS Code): - Replace , , and with your actual credentials. - For OSS installations, you can use KESTRAUSERNAME and KESTRAPASSWORD instead of KE

A Python implementation of the Model Context Protocol (MCP) for ROS 2. This server enables AI tooling to connect with ROS 2 nodes, topics, and services using the MCP standard over stdio. Designed to be the easiest ROS 2 MCP server to configure. - List available topics - List available services - Lists available actions with their types and request fields - Call services - Subscribe to topics to co

MCP Server that enables LLMs to interact with the local filesystem. Provides tools for navigation, search, file management, and analysis — all scoped to allowed directories. Filesystem MCP exposes a rich set of tools for reading, writing, searching, and inspecting files and directories. All operations are strictly bounded to the directories you provide at startup, preventing access to any path out

MCP-сервер для Altegio API — управление записями, клиентами, услугами, сотрудниками и расписанием через AI-ассистента. - 18 MCP-инструментов — записи, клиенты, услуги, сотрудники, расписание, финансы - CRUD-операции — полный цикл создания, чтения, обновления и удаления записей и клиентов - Умный поиск — автоопределение типа запроса (телефон, email, имя) - Docker-образ — multi-stage build на Alpine

A Node.js server implementing the Model Context Protocol (MCP) for interacting with the storacha.network platform via the w3 command-line interface (@web3-storage/w3cli). This server empowers language models 🤖 and other MCP clients to manage storacha.network spaces, upload/download data, manage delegations, and perform various other tasks by seamlessly wrapping w3 commands. - Wraps the w3 CLI for

A Model Context Protocol (MCP) server for converting Markdown content to interactive mindmaps. Or using uvx: Or using docker safer and easier. Three installation methods have been successfully tested on macOS and Linux. For Windows users experiencing issues with npx for this MCP, consider using the Docker method. Alternatively, if you use Visual Studio Code, the "Markmap" extension offers a potent

An MCP server that provides tools for checking latest stable package versions from multiple package registries: - npm (Node.js/JavaScript) - PyPI (Python) - Maven Central (Java) - Go Proxy (Go) - Swift Packages (Swift) - AWS Bedrock (AI Models) - Docker Hub (Container Images) - GitHub Container Registry (Container Images) - GitHub Actions This server helps LLMs ensure they're recommending up-to-da

A Model Context Protocol (MCP) server that integrates with Tilt to provide programmatic access to Tilt resources and logs through LLM applications. Imagine prompting like this: The key insight is you no longer need to tell your LLM how to build and deploy your code. Instead, you can simply ask it to what to build and deploy. Tilt is a powerful tool for working with Docker/Kubernetes workloads. Wit

Shodh-Memory Persistent memory for AI agents. Single binary. Local-first. Runs offline. We built this because AI agents forget everything between sessions. They make the same mistakes, ask the same questions, lose context constantly. Shodh-Memory fixes that. It's a cognitive memory system—Hebbian learning, activation decay, semantic consolidation—packed into a single ~17MB binary that runs offline

Dependency intelligence infrastructure for AI assistants and agents working with JVM projects. Traditional upgrade workflow: 1. Google "Spring Boot 2.7 to 3.2 migration guide" (5 minutes) 2. Read 50-page migration docs, cross-reference breaking changes (15 minutes) 3. Check Maven Central for compatible dependency versions (5 minutes) 4. Update pom.xml, compile, fix breaking changes (30 minutes) 5.

- Requirements - Entity Operations - Telemetry Management - Relations - OTA Packages - Entity Data Query - Quick Start Guide - Installation - Docker Image - Build from Sources - Client Configuration - Binary Configuration - Docker Configuration - Environment Variables - Tool Groups Configuration - Available Tools - Device Tools - Asset Tools - Customer Tools - User Tools - Alarm Tools - OTA Tools

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