"Methods to Improve Development Productivity Using MPC (Model Predictive Control)" matching MCP servers:

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.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

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.

An MCP server that enforces development discipline and workflow best practices, guiding users through a structured process of coding, testing, documenting, committing, and releasing.

A FastAPI-based JSON-RPC 2.0 server implementation that enables users to work with HDF5 files, submit Slurm jobs, retrieve CPU information, and visualize CSV data through standardized API endpoints.

Provides secure, controlled access to GitHub operations through the Model Context Protocol with enterprise-grade security features including secret detection, vulnerability scanning, rate limiting, and full audit trails. Supports repository management, file operations, branch creation, commits, and GitHub Actions workflows.

Enables AI agents and external systems to programmatically trigger and monitor Jenkins jobs, retrieve build status and logs via MCP standards.

Enables AI coding agents to orchestrate the full software development lifecycle on GitHub, including planning, issue creation, code review, security triage, and release readiness checks.

An MCP server that provides deterministic tools for AI assistants to standardize development workflows, including branch naming, commit validation, and PR checklists.

An MCP server designed for React Native and Expo development that provides specialized tools for project scaffolding, architectural best practices, and troubleshooting. It enables AI assistants to guide users through setup, navigation configuration, and CI/CD processes using modern stacks like NativeWind and Zustand.

Model Context Protocol (MCP) server for Octopilot — enables AI agents to detect, generate, build, and wire up new repositories end-to-end using the Octopilot CI/CD toolchain.

A Model Context Protocol (MCP) server for LocalStack that enables management of local AWS development environments, including container lifecycle operations, infrastructure deployments, log analysis with IAM policy generation, chaos injection, and Cloud Pods state management.

An MCP server that automates the full software development lifecycle through an AI-driven TDD state machine. It handles everything from task decomposition and test-driven development to integration testing and automated pull request creation.

Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.

Provides structured access to Xcode development tools including xcrun, xcodebuild, and xctrace for building, testing, and analyzing projects.

Provides structured build, compile, and test operations with parsed output to reduce token usage and improve readability.

Provides structured project management through phase-based workflows, enabling planning, execution tracking, compliance checking, and automated documentation for software development projects.

A demonstration MCP server built with FastMCP v2.0 that provides basic mathematical calculations and greeting functionality. Features Docker containerization, comprehensive testing, and CI/CD automation for learning MCP development patterns.

Integrates Honeybadger error tracking with Cursor IDE, allowing developers to fetch, analyze, and troubleshoot application errors directly from their development environment.

Enables monitoring and control of the SST development process by streaming dev logs, managing Lambda invocations, and executing lifecycle commands like start, stop, and restart. It provides real-time access to event streams and deployment status directly through the Model Context Protocol.