"Construct 3" matching MCP servers:

An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.

Enables autonomous AI agents and developers to interact with the GitHub REST API for repositories, pull requests, issues, actions, releases, tags, and user/org data through MCP tools. It uses token-efficient response summaries and optional 3-tier read-only safeguards to keep interactions safe and context-window friendly.

Checks how long ago a package version was published and returns the newest version older than a minimum age (3 days by default), so an agent avoids brand new releases when adding dependencies. Also checks lockfiles across 15 ecosystems; read-only.

Enables Claude to interact with R package development workflows, including CRAN metadata lookup, dependency checking, testing, documentation validation, and release automation.

A secure MCP server providing controlled access to pixi tasks and commands, eliminating agent bash circumvention. It exposes 11 pixi tools through 4 meta-tools for ~95% context savings.

Exposes the App Store Connect API as MCP tools for reading team accounts, apps, builds, groups and signing assets, and for creating bundle IDs, certificates and provisioning profiles or uploading builds. Write calls are gated on owner approval and the server runs as a host-side service with the API key kept outside the agent's reach.

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Enables MCP-compatible AI clients to manage GitHub repositories and issues and trigger repository dispatch events via tools and a REST API.

Token-efficient GitLab MCP server that delivers 167 tools through 3 meta-tools with progressive disclosure, field projection, server-side file trimming, and keyset pagination for agent context budgets.

Enables running and managing automated tasks with retry loops and machine-checkable success criteria via MCP tools.

Enables triggering GitHub repository dispatch events, creating issues, and retrieving repository information.

Enables Claude Code agents to judge AppStore staging rollouts by reading Argo CD, Kubernetes pod/rollout/log/event, CloudNativePG, health, certificate, node, and GitHub Actions/PR data through read-only, redacted tools.

Enables MCP clients to score retrieval quality with recall, precision, nDCG, hit rate and MRR, compare 2-3 configurations with seeded paired-bootstrap confidence intervals, audit agent tool calls against expected traces, and detect prompt regressions against golden sets. It also generates Markdown, JSON or JUnit reports with pass/fail gates, all deterministically and offline without paid API keys.

Connects AI assistants to GitLab, enabling natural language queries for merge requests, code reviews, pipeline tests, job logs, and commit discussions directly from chat.

Read-only MCP server for searching and listing components in Nexus Repository Manager 3 (Docker, Maven, npm, PyPI, etc.) with tools for repositories, components, Docker tags, and latest version.

Enables interaction with Azure DevOps, including listing pipelines and runs, viewing logs and artifacts, collecting and normalizing security reports, and triggering pipelines with safety controls.

Unified MCP server for Git forge management across GitHub, GitLab, Gitea, and Bitbucket, enabling repository, issue, PR, and CI/CD operations via CLI tools and APIs.

Enables scanning Python repos for dead code and automatically drafting a branch + PR for the safe subset (unused single-name imports) after running the repo's full test suite. All other findings are reported without modification, and a human always merges.

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 CI/CD failure triage for GitHub Actions by inspecting failed runs, classifying root causes with LLM assistance, retrieving relevant runbook guidance, estimating flakiness, and either preparing dry-run fix PRs or publishing structured diagnoses to Discord for human review.