Enables an AI assistant to author algorithm storyboards, workflow maps, and UI styling through MCP, serving a local web app for visualizing and reviewing them.
Enables deterministic, resumable workflow automation through DAG step graphs, boolean gate branching, automatic parallel execution, and in-step agent loops with sandboxed file and command tools.
An MCP server for N8N that allows users to build, manage, and execute workflows through natural language, with 18 tools covering CRUD, execution, and intelligent workflow construction using templates, scaffolds, or LLM-based generation.
Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.
An MCP server that turns deployed n8n workflows into callable tools for AI agents, enabling discovery and execution of tagged workflows via a standard interface without rewriting integrations.
MCP server providing access to n8n documentation and workflow templates, enabling LLM agents to search, read, and retrieve workflow examples for automated workflow creation.
Manages workflows, executions, and folders on a self-hosted n8n instance through its public REST API, enabling MCP clients to create, update, activate, list, and trigger workflows via natural language.
Lets AI agents read, create, and update n8n workflows with a guarded two-phase plan-and-apply flow that backs up every change, refuses writes when the live workflow has moved, and verifies results by readback. Also supports rehearsing changes on trigger- and outbound-disabled test clones, so mutations to a live instance stay reviewable and reversible.
Enables AI agents to operate the Unreal Editor via MCP: reading, creating, and editing blueprints at the node level, authoring materials and UMG widgets, auditing projects, and verifying runtime behavior in PIE with read-back checks.
Windows desktop automation MCP server enabling screenshot capture, precise visual locating of UI elements via vision models, and automated mouse clicking.
Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.
Enables AI agents to create private GitHub issues for decisions, send email alerts, wait for human comments, and parse replies so work can resume. Supports guided setup and MCP integration without requiring separate AI API keys or an always-on cloud server.