"How to operate a computer and execute commands" 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.

Model Context Protocol (MCP) server for GitLab — exposes 1006 GitLab REST & GraphQL API operations as MCP tools (42 meta-tools / 57 enterprise), 24 resources, 38 prompts, and 17 completion types for AI assistants. Written in Go, single static binary, stdio and HTTP transport.

An MCP server that enables AI assistants to discover and execute Nox sessions for project automation tasks like testing, linting, and building. It provides tools to list available sessions and run them using specific Python versions, tags, or keyword expressions.

MCP server for Argo CD and Vault that allows authenticated users to run CLI commands via GitLab SSO, with confirmation for dangerous operations and secret redaction from model context.

Enables interaction with Rundeck instances to list jobs, execute jobs with options, and retrieve execution status and logs through natural language commands.

Enables interaction with Gradle projects through the Gradle Wrapper, allowing users to list projects and tasks, execute builds, run tests, and clean artifacts with real-time progress reporting and comprehensive error details.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

Enables AI assistants to manage and operate Venelx CI/CD pipelines through the Venelx REST API, including listing projects, triggering builds, checking status, tailing logs, accessing artifacts and workers, and diagnosing signing or GitHub access.

A local MCP server that bridges Claude Desktop/Cowork to the Azure DevOps CLI, enabling full use of az devops, az repos, az boards, az pipelines, and az artifacts commands.

Enforces authorize-before-execute for any MCP-compatible AI agent. Exposes atlasent_evaluate and atlasent_verify_permit tools so a protected tool call only runs after AtlaSent issues and verifies a signed, single-use permit — fail-closed on any error, timeout, or malformed response.

Provides secure access to containerized build environments for software projects, enabling AI assistants to execute builds, run tests, manage git operations, and inspect build artifacts without requiring local installation of dependencies.

Enables AI coding assistants to interact with JetBrains TeamCity CI/CD server through natural language commands. Supports triggering builds, monitoring status, analyzing test failures, and managing build configurations directly from development environments.

Enables AI systems like Claude and Cursor to directly manage Railway projects, deployments, services, environment variables, and monitor logs through natural language commands.

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.

Enables management of Atlassian Bamboo CI/CD pipelines, projects, plans, builds, and deployments through natural language commands.

Connects TestDino test management platform to AI agents, enabling users to check test runs, analyze failures, upload Playwright test results, and manage test cases through natural language commands.

Enables comprehensive Jenkins automation through MCP interface, allowing users to manage builds, jobs, artifacts, and queues with natural language commands. Supports build triggering, monitoring, artifact retrieval, and system status checks with automatic CSRF protection and authentication.

A Model Context Protocol server that integrates with AWS CodePipeline, allowing users to manage pipelines through Windsurf and Cascade using natural language commands.

Enables AI assistants to manage development workflows by running build commands, executing tests, analyzing package.json files, installing dependencies, and performing code linting. Supports multiple package managers (npm, yarn, pnpm) and provides detailed error reporting for development operations.