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

Multi-persona engineering mentor with 64 specialized AI personas that injects engineering standards before Claude reasons, transforming passive documentation into active collaborative guidance.

A specialized MCP server for QA engineering. It provides tools for user story analysis, test strategy, BDD/Gherkin scenarios, contract testing, integration tests, performance plans, security checklists, and CI/CD pipeline generation, accessible from any MCP-compatible IDE or client.

Enables interaction with the Tembo API to manage organization tasks and view enabled code repositories. Users can create, search, and list tasks or retrieve account information directly through MCP-compatible clients.

Enables interaction with Blacksmith CI analytics to query workflow runs, jobs, test results, and usage metrics. It provides detailed access to CI/CD data including job logs and billing information directly through Claude.

A Model Context Protocol server that allows management of Netlify sites, enabling users to create, list, get information about, and delete Netlify sites directly from an MCP-enabled environment.

A Model Context Protocol server that enables integration with GitHub Actions, allowing users to fetch available actions, get detailed information about specific actions, trigger workflow dispatch events, and fetch repository releases.

Enables AI assistants to interact with Terraform Cloud workspaces and runs, including checking run status, listing workspaces, and retrieving detailed information about workspaces and runs.

Enables querying Swarmia's Export API for engineering metrics including pull request analytics, DORA metrics, investment balance, and effort reporting through an MCP interface.

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.

Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.

A production-ready template for building Model Context Protocol (MCP) servers in Python, using Docker Compose for containerized development and CI/CD.

Enables automated analysis of Woodpecker CI pipeline failures with intelligent error detection and fix suggestions. Supports both direct pipeline analysis and IDE-integrated git-context analysis using repository names, PR numbers, or branch information.

An MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.

Query Prometheus from CLI or any MCP client, with retries, caching, and an optional metrics exporter.

Enables retrieving comprehensive information about GitLab merge requests, including details, commits, diffs, discussions, approvals, and CI/CD status, through natural language.

Automates remediation of Java library vulnerabilities by processing a security advisory Excel export, scoping to a Spring Boot Maven app, applying version upgrades to pom.xml, and verifying the build.

A boilerplate for building FastMCP servers, providing a basic setup for testing, linting, formatting, and publishing to NPM.

Enables LLM clients to access DevSecOps tooling such as CI/CD pipeline status, vulnerability triage, log search, and dependency scanning through MCP, turning AI copilots into security-aware engineering partners.

Enables agents to search, retrieve, and execute commands for Harness Engineering skills.

A thin TypeScript MCP server that exposes engineering tools (Bitbucket, Jira, Confluence, ArgoCD) to MCP-compatible editors via a single API key per developer, delegating all service interactions to an internal backend.