"Information about Flowable (e.g., workflow and business process management tool)" matching MCP servers:

Local MCP server for safe inspection and dry-run cleanup of leftover subprocesses from AI coding agents, providing ownership-based classification and reproducible evidence.

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 bounded, multi-phase coding workflows inside Codex that plan, implement, independently review, repair, and verify repository changes, with an inline dashboard for inspecting runs.

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

Enables agentic coding clients to enforce portable policy guardrails by evaluating proposed shell, file write, git, and network actions and returning allow, deny, or ask decisions.

Guides agents through structured workflows with predefined steps, allowing for flexible execution via instructions and tool calls.

A server built on mcp-framework that enables integration with Claude Desktop through the Model Context Protocol.

Delegates autonomous coding tasks to Z.ai's GLM models with a genuine agent loop, file operations, and verification via git and real process results.

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

Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.

Provides specialized tools for portfolio health analysis, rebalance simulations, and trade execution via a single interface powered by MongoDB. It enables AI agents to manage investment portfolios while adhering to organizational governing rules, clearance guards, and rate limits.

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

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.

Enables seamless integration between Claude AI and Dify Workflow through the Model Context Protocol, allowing Claude to dynamically access and execute Dify Workflow operations via natural language.

Enables deterministic, step-by-step execution of YAML-defined skills and workflows for AI agents, with progressive disclosure of active-step instructions and tools. It manages workflow state, conditional DAG transitions, loops, and persistent sessions via start_workflow, next_step, and end_workflow.

Provides a no-op 'think' tool that enables Claude to decompose complex problems and enhance reasoning by pausing to organize thoughts.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Implements Anthropic's 'think' tool for Claude, providing a dedicated space for structured reasoning during complex problem-solving tasks that improves performance in reasoning chains and policy adherence.

An MCP server implementation of Anthropic's Think Tool prompt engineering technique that enables Claude to break down complex problems and enhance its reasoning capabilities by providing a simple tool that echoes back thoughts.

This is a powerful Model Context Protocol (MCP) server that integrates multiple AI coding agents—Anthropic Claude Code, OpenAI Codex, and Google Gemini—directly into your workflow. It enables seamless cross-provider analysis, leveraging Gemini's massive token window, Codex's specialized coding capabilities, and Claude's advanced reasoning.