"Spec-WorkFlow documentation or implementation" matching MCP servers:

Streamlines development workflows through AI-assisted codebase analysis, comprehensive planning, task breakdown with dependencies, and automated implementation verification. Enables systematic approach to complex development tasks like framework migrations and feature implementation.

An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.

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

Automates GitHub workflows (branch creation, testing, commit/push, PR) through natural language instructions in Claude Code.

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.

Provides a specification-driven workflow layer for AI-assisted coding, enabling agents to follow an explicit 11-phase feature workflow with checkpoints, artifacts, and quality gates.

Acts as a unified dispatch hub for local coding agent CLIs, supporting capability-based routing, schema validation, and retries via MCP tools.

Automatically generates compelling application specifications and code based on the four dimensions (existence, historicity, relationality, irrationality), simplicity, and Figma-like UI, enabling users to create great apps without detailed instructions.

An MCP server that implements a structured workflow for LLM-based coding, guiding development through feature clarification, documentation generation, phased implementation, and progress tracking.

Enforces a git-tracked, code-checked finite state machine that hands control between a planning director and a local Ollama implementor, exposing tools to check phase status, transition states, and run the implementor to execute plan step files one at a time. It enables a project to be planned, implemented step by step with per-step test gating, and validated before completion.

Enables Codex to delegate review and implementation tasks to the local Qoder CN CLI as background jobs, with two permission tiers and polling-based status updates.

An MCP server that splits coding work between ChatGPT web (planning, review) and Codex CLI (implementation), enabling agentic workflows over a local repo with risk-gated execution and safety rails.

A stdio MCP server that lets Codex Desktop/CLI delegate implementation tasks to Claude Code CLI with workspace validation, Git status checks, and session resume capabilities.

Exposes the local Claude Code CLI as a subagent for Codex, enabling background review and implementation jobs with two fixed permission tiers.

MCP server that lets Claude Code ask GPT Codex for adversarial planning, code review, debugging, research, and risk triage without leaving your project workflow.

An MCP bridge that enables AI coding agents to delegate implementation tasks to the OpenAI Codex CLI, allowing the orchestrator to plan and review while Codex executes.

Bridges Claude Code and OpenAI Codex CLI for an interactive plan-execute-review workflow, enabling Claude to interview, design, and review while Codex implements code changes.

Official implementation of Anthropic's 'think' tool that provides Claude with a dedicated space for structured reasoning, improving performance by up to 54% on complex tasks requiring multi-step problem solving.

Provides unified access to multiple CLI AI agents (Codex, Gemini, Claude, and OpenCode) through a single MCP interface with real-time task monitoring, enabling specialized code analysis, UI design, implementation, and prototyping workflows.

Enables AI tools to interact with browsers for enhanced frontend development, providing context to LLMs through tools like API call analysis, screenshots, element selection, and documentation ingestion.