"An automation platform for workflow and task management" matching MCP servers:

Enables approval-gated incident response workflows that gather evidence through read-only MCP tools, perform idempotent writes, and preserve a durable audit trail.

A Model Context Protocol server that exposes AI workflow templates as callable tools, enabling MCP clients to list templates, inspect their input/output schemas, and execute them synchronously or asynchronously to get structured results.

An MCP server for task orchestration.

Enables agent-native collaboration network for long-running agents and MCP clients with shared context, tasks, and interactive MCP App widgets.

A standalone runtime and MCP server that executes Claude Code dynamic workflow files through interchangeable agent providers, ensuring compatibility with Claude Code's workflow format.

Enables multi-agent code review and iterative improvement loop using different LLM backends, with deterministic scoring and audit trail.

Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

Enables AI agents to manage live Firefox tabs and native tab groups via MCP tools over stdio, with exact-match verification and safety constraints. It does not read page content or send browser data remotely.

This MCP server enables agents to manage complex tasks by providing tools for registration, complexity assessment, breakdown into subtasks, and status tracking throughout the task lifecycle.

Enables AI agents to integrate with IBM Business Automation Workflow by exposing workflow REST services as MCP tools, allowing natural language interaction with business automation capabilities.

Enables AI agents to create, share, discover, and execute reusable multi-step workflow templates.

A production-grade multi-agent workflow orchestrator built on the Model Context Protocol, featuring planner/executor/critic agents, durable run state, and replayable traces.

Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.

A local MCP server that forwards tools from an upstream experiment platform and adds AI Agent tools for experiment analysis and skill debugging.

Enables ChatGPT to inspect Jobber data and manage controlled automations with scheduling, approval workflows, and audit trails.

Enables MCP clients to run complete browser workflows through a single run_workflow tool, with anti-detection backends, intelligent element location, automatic retries, and session persistence.

Enables AI coding agents from different MCP-capable clients to exchange messages in real time through a shared in-memory broker. Supports agent registration, inboxes, and live event streaming without requiring a database.

A shared communication bus for multiple agents using NATS and MCP, enabling MCP clients to list, message, and query agents.

Enables enterprise multi-agent decision workflows that expose 8+ MCP tools such as SQL query, web search, Python sandbox, RAG, file access, data cleaning, chart generation, and HTTP calls, orchestrated with LangGraph, streaming output, and human-in-the-loop approvals.