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

Provides structured spec-driven development workflow tools for AI-assisted software development with sequential spec creation (Requirements → Design → Tasks). Features a real-time web dashboard for monitoring project progress and managing development workflows.

An intelligent MCP server that helps development teams maintain high-quality project documentation by providing an AI-powered workflow for creating comprehensive specifications through requirements, design, and implementation documents.

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

A comment-based spec-driven development MCP server that facilitates workflows from requirements creation to implementation via AI, using @spec-impl markers.

Enables AI coding tools to follow a structured spec-driven development workflow with three phases: requirements, design, and tasks, ensuring approval before advancing.

A deterministic MCP server that checks project code alignment with SPEC documents through static analysis without AI involvement.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

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 AI coding agents to collaboratively create and manage a specification roadmap persisted in PostgreSQL, progressing from constitution through specification, clarification, planning, task generation, and analysis, while exposing a get_next_task tool for implementation sessions.

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.

Centralized MCP server for spec-driven AI agent workflows, enabling isolated feature management, task tracking, and implementation with handoff and archiving capabilities across multiple projects and developers.

A methodology and MCP server for agent-driven software development where humans write specs and agents implement code, enforced by six mechanical gates to ensure spec validity, contracts, tests, and review.

Automatically analyzes Python codebases and generates professional documentation including README, API docs, and setup guides.

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files.

Search and retrieve Agent2Agent (A2A) protocol documentation using full-text search and section filtering.

AI 規格大師 — MCP server bridging specs (Linear / JIRA / GitHub Issues / Notion / Markdown / Figma) to tests, with bidirectional traceability and a spec-quality coach. Sibling to mk-qa-master.

MCP server for Claude Code that generates a documentation skeleton from Git repository changes, outputting a Markdown file with placeholders for AI analysis.

Architecture diagram with concurrency capacity and bottleneck estimates from any codebase.

An MCP server that provides AI assistants with searchable access to the full s&box game engine documentation and API reference.