"Open Collective" matching MCP servers:

Exposes open automation platform capabilities to AI agents with tools for managing job templates, inventories, collections, EDA activations, audit events, and policy checks, governed by gateway auth, RBAC, and policy enforcement.

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.

Baron gives an AI coding agent one normalized contract for work orchestration: create and transition issues, cut branches, open and merge pull requests, and read CI and deployment status across Azure DevOps, GitHub and Slack.

An MCP server that drives Google Play releases: bundle upload, track releases (internal/closed/open/production), listing metadata, store graphics, and review replies.

Enables real-time assessment of GitHub repository health by exposing tools and resources for repository metadata, open issues, recent commits, and GitHub Actions workflow runs, plus prompts for health reports and issue triage.

Checks the outcome after an agent handoff, when a request, 200 or ack only shows the handoff. Reads one field of one exact public HTTPS JSON record, with a same-site known-good control, for 20-120 s. Returns PROVED, DISPROVED or INCONCLUSIVE with a signed receipt you can verify. 5 free checks, then USD 1 per completed check, by card (Stripe Checkout) or existing Link wallet. INCONCLUSIVE is free.

LoopSense is an open-source MCP server that closes the feedback loop for AI coding agents — giving them real-time visibility into CI results, deployments, test outcomes, and file system changes.

Connects AI tools directly to Codemend production error monitoring to list, analyze, and resolve software crashes. It enables users to retrieve AI-generated fixes and automatically open GitHub pull requests to address production issues.

Enables AI agents to read repositories, manage issues, inspect CI/CD workflow runs, create branches, commit files, and open pull requests on a self-hosted Gitea instance.

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.

Enables agents to open production change dossiers, attach proof certificates, and request human approval through a secure, unforgeable gate—ensuring no irreversible change can proceed until it has been verified against a sandboxed shadow copy.

An open source MCP server empowering SREs with intelligent observability, predictive analytics, and AI-driven automation across Kubernetes, OpenShift, and Tekton environments.

Protect against malicious open source packages 🤖

Tells your coding agent which tests to run after a change, and whether a failing test is its fault. Tools: select_tests (only the tests the change can affect, with reasons), triage_failure (new regression, pre-existing, flaky or unknown) and explain. Needs a self-hosted siftwise server; fails open to the full suite. Tested with Claude Code, Copilot, Cursor, Codex and Gemini.

An open-source server that enables AI agents to interact with Azure DevOps projects through the Model Context Protocol, providing tools for managing work items, wikis, and repositories to streamline development workflows.