"Metallurgical Engineering and Steel Plant Processing Information" matching MCP servers:

Enables local engineering workflow management by consolidating tickets, QA evidence, time tracking, root cause investigation, knowledge, and reporting into a single SQLite database, allowing generation of complete ticket packages for handoffs, dailies, or career evidence.

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

Enables AI to read live weather data and manage a persistent to-do list through MCP tools, demonstrating both read and write capabilities without API keys.

Enables querying Keypup engineering analytics (delivery, quality, team workload) using natural language through MCP-compatible AI assistants.

A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.

Enables AI assistants to interact with OpenProject installations for comprehensive project management, including creating projects and work packages, managing users and assignments, creating dependencies, and generating Gantt charts through natural language commands.

Provides AI clients with per-project todo management and persistent context notes via MCP, enabling task tracking and resume summaries.

Enables AI assistants to retrieve Rally work items by FormattedID using HTTP Basic Authentication.

A collection of MCP servers for Slack, Jira, Confluence, and release coordination to automate QA workflows and daily release status reporting.

DevsContext is an MCP server that provides AI coding agents with synthesized engineering context—requirements, decisions, architecture, and standards—from tools like Jira and Slack. It fetches and synthesizes relevant information on demand to help agents work on tasks correctly.

An MCP server that turns independent AI agents into a coordinated engineering team with shared task board, context, review loop, and enforced plan-implement-review-iterate workflow.

An MCP server that gives LLM agents structured, safe, and traceable access to engineering project documentation stored in Markdown/Git repositories, enabling management of requirements, decisions, tests, tasks, and impact analysis.

Dynamic context and active engineering workflow for AI coding assistants, enabling project-aware rules, code review, TDD, and delivery orchestration.

The intelligent execution layer for coding agents, exposed as an MCP server for high-stakes engineering projects. It enables AI agents to manage plans, tasks, and integrations via tool calls.

Connects your Ashby recruiting data to Claude, enabling natural language queries and management of candidates, applications, jobs, interviews, offers, and team information.

Enables AI assistants to interact with IBM Engineering Lifecycle Management (ELM), including DOORS Next Generation, EWM, and ETM, for managing requirements, work items, tests, and project builds through natural language.

A federated MCP server that combines IBM's engineering-ai-hub tools with its own authoring and orchestration tools for IBM ELM, enabling AI hosts to manage requirements, models, and compliance through a single interface.

Enforces engineering governance for AI-driven software projects, ensuring state over prompt, freeze over generate, and audit over output through the 5S workflow.