Complete AI workflow system with 100% guide integration - all 13 maxims, 3 heuristics, operational flexibility, artifact management, and nested workflows
Enhanced MCP Workflow Server with smart problem routing, comprehensive validation, guide compliance, and robust error handling. Intelligently routes to appropriate AI functions based on problem type.
A simple Hello World MCP server
Integration between n8n workflow automation and Model Context Protocol (MCP)
The official TypeScript library for the Cloudflare API
A TypeScript SSE proxy for MCP servers that use stdio transport.
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Model Context Protocol implementation for TypeScript
Playwright Tools for MCP
Simple to use, blazing fast and thoroughly tested websocket client and server for Node.js
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mcp-ui Client SDK
Command-Line Interface for Firebase
The official TypeScript library for the Turbopuffer API
Model Context Protocol inspector
A high-level API to automate web browsers
A high-level API to automate web browsers
A Model Context Protocol (MCP) server implementation for CircleCI, enabling natural language interactions with CircleCI functionality through MCP-enabled clients
MCP server for filesystem access
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MCP server for Context7
mcp-ui Server SDK
MCP server for interacting with Azure DevOps
A simple zero-configuration command-line http server
Rails Engine that captures exceptions, stores them in your database with rich context, and exposes error data via a bundled MCP server so AI agents can triage, resolve, and fix errors autonomously.
A Ruby MCP server that wraps Linear's GraphQL API and returns TOON-formatted responses for ~40-60% token savings in LLM workflows.
Agentf is a Ruby-native multi-agent workflow engine with an ORCHESTRATOR, role-specialized agents, provider adapters (OpenCode/Copilot), and Redis-backed semantic, episodic, and graph-style memory. It includes a unified CLI, MCP server tools, and install/update workflows for generated agent/command manifests.
RobotLab is a Ruby framework for building and orchestrating multi-robot LLM workflows. Built on ruby_llm, it provides robots with template-based prompts, tools, and shared memory; networks for coordinating multiple robots with intelligent routing; MCP (Model Context Protocol) integration for external tool servers; and a memory system with Redis backend and semantic caching. Optional gems add Rails integration (robot_lab-rails), durable learning (robot_lab-durable), Ractor concurrency (robot_lab-ractor), and document storage (robot_lab-document_store).
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