"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.

A portable, stdio-based MCP server that exposes curated read operations across Microsoft Graph, Power BI, and Dataverse, plus an optional create-only Gemba action tool, using explicit Entra identities and read-only defaults. It lets teams run enterprise engineering workflow steps—discovery, draft preparation, and reviewed progress tracking—with separately approved, permission-scoped connectors and no automatic tenant access.

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 agents and humans to inspect measured-room projects, update bounded dimensions and assumptions, and export quantity schedules and revision dossiers through constrained tools with stale-edit and replay protections.

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

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.

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 agents to read and report, and to carry human-authored updates into systems of record after human consent, while structurally preventing autonomous writes of commitments like owners, due dates, or statuses.

Enables agentic co-authoring of feature specs, decomposition into architecture-validated tickets, and idempotent syncing to Linear as projects with epics, sub-issues, and dependencies.

Fast native Swift OmniFocus MCP server and CLI for macOS. Let AI assistants safely read, update, complete, and organize tasks and projects through documented Omni Automation APIs.

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.

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.

Gives MCP-compatible AI assistants full, typed access to OmniFocus on macOS, enabling task management, project manipulation, inbox processing, and more via natural language.

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.

Enables AI assistants to inspect assets and alarms and to create, assign, and transition work orders through a role-checked life cycle with an audit trail. Runs locally over MCP stdio against seeded fictional plant data, with optional read-only and persistent-database modes.

Bridges local development environments with the Engineering Intelligence Platform (EIP), enabling AI coding agents to manage SSOT, generate implementation/test/security plans, and synchronize architectural knowledge.

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.

Enables MCP-compatible clients to delegate senior software engineering tasks like code generation, review, Jira management, and architecture recommendations to a model with a senior engineer persona.

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

Provides capabilities for searching Jira issues using JQL and retrieving detailed issue information.

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.

On-prem bridges for the legacy software-engineering toolchain, plus the Evidence Weaver that joins them.

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.