"Guidelines for Interfacing with Jenkins" matching MCP servers:

MCP server for Jenkins (with optional GitLab integration) that lets AI assistants list jobs, trigger and track deployments, fetch build logs, and verify whether tickets are deployed, without touching the Jenkins UI.

The Model Context Protocol (MCP) Jenkins integration is an open-source implementation that bridges Jenkins with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Jenkins tools while maintaining data privacy and security.

MCP server that triggers Jenkins automation jobs and returns their results, with an action registry, parameter validation, and credential redaction.

Exposes a Jenkins controller's REST API to MCP-compatible clients, enabling job management, build triggering, and log retrieval through natural language.

A comprehensive MCP server with 53 tools for managing Jenkins CI/CD operations, including job, build, pipeline, node, credential, and plugin management, enabling AI assistants to control Jenkins servers through natural language.

Enables interaction with Jenkins servers through HTTP APIs, allowing users to list jobs, trigger builds, view logs, and manage configurations without requiring plugins or admin access.

An MCP server that enables LLMs to automate browser interactions using Playwright to fetch real-time Israeli weather data.

Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

Unofficial Powershell MCP server

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

MCP server that bridges Ableton Live with AI models, enabling real-time project inspection and control such as track overview, device parameters, and audio analysis.

Enables AI-driven 3D modeling and scene creation in Blender, allowing LLMs to create, modify, and manipulate objects, materials, and assets through natural language.

Provides aerodynamic analysis tools through MCP, enabling geometry generation, meshing, CFD solving, and visualization for 2D airfoils.

Provides a local MCP server for AI-powered web crawling on Windows, enabling agents to directly crawl and extract web content without requiring WSL.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A minimal MCP server that exposes a run_command tool over stdio, enabling command execution within a Docker container.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Enables controlling CLO3D from any MCP client to inspect projects, create and edit patterns, manage fabrics and colorways, run simulations, and export models through chat.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.