"Guidelines for Interfacing with Jenkins" matching MCP servers:

MCP server that connects AI agents to Jenkins, supporting stdio and HTTP transports with tools for jobs, builds, logs, and queue management.

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.

A server that enables interaction with Jenkins CI/CD pipelines from any compatible MCP client (like Claude Desktop), allowing users to manage jobs, builds, coverage reports, and other Jenkins functionality through natural language.

Enables managing Jenkins operations like listing jobs, triggering builds, and checking build statuses through a configurable MCP server.

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.

Provides AI assistants with full access to Jenkins CI/CD systems, enabling querying, managing, and controlling Jenkins jobs, builds, nodes, and queues through natural language.

MCP server for Jenkins to inspect builds, control jobs, and manage pipeline configuration.

MCP server for managing multiple Jenkins instances via REST API, supporting job/build queries, triggering builds, and logs with optional read-only mode.

Enables interaction with multiple Jenkins instances from a single MCP server, using header-based authentication for multi-tenancy.

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

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

Provides code review and guideline loading based on Andrej Karpathy's coding principles, helping LLMs avoid common pitfalls like over-engineering and unnecessary changes.

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

A multi-Jenkins server management and automation tool that enables intelligent scenario mapping, multi-server management, and complete CI/CD lifecycle operations through natural language interactions.

A Jenkins MCP server with 37 tools for managing jobs, builds, nodes, views, and CI/CD workflows.

Enables AI assistants to interact with Jenkins CI/CD servers, including checking build status, triggering builds, and retrieving build logs.

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 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 AI coding tools to interact with Jenkins CI instances behind Cloudflare Access, supporting job inspection, build logs, test results, and artifact retrieval.

Enables interaction with Jenkins instances to manage jobs, builds, and nodes through the Model Context Protocol. It provides capabilities to trigger builds, read console outputs, and search log sections using regular expressions.