"The Models Resource" matching MCP servers:

Manages resource allocation, MCP server lifecycle, and Kubernetes workers in cortex automation systems. Provides tools for requesting/releasing job resources, starting/stopping/scaling MCP servers, and provisioning/destroying burst workers with TTL management.

Provides AI agents full access to the Harness.io platform through 10 consolidated tools and 137 resource types, enabling management of CI/CD, GitOps, Feature Flags, cost data, and more.

Enables AI assistants to manage website deployments over SFTP/FTPS by listing sites, testing connections, comparing files, previewing changes with approval tokens, creating SHA-256 verified backups, and executing authorized uploads.

An MCP server that gives AI agents full access to the Harness.io platform through 11 consolidated tools and 207 resource types.

MCP server for DCI API enabling AI models to extract and analyze DCI jobs, components, and topics, with integrations for Google Drive, Jira, GitHub, and Red Hat support cases.

A local MCP server that packages LLM evaluation gates as reusable CI/CD primitives, enabling AI agents to run datasets against models, score responses, and enforce quality thresholds.

An MCP (Model Context Protocol) server that integrates with the ArgoCD API, enabling AI assistants and large language models to manage ArgoCD applications and resources through natural language interactions.

Self-hosted coding agent that runs Claude Code or Codex in your own infrastructure and works from Slack. Exposes codebase investigation, file edits, branch and PR creation, allowlisted command execution, CI status and an audit log as MCP tools, keeping every credential behind the tool layer instead of handing it to the model.

Provides Xcode build, test, and package management operations through the Model Context Protocol.

Enables AI assistants to review GitLab merge requests by fetching changes, analyzing diffs, adding comments, and managing approvals through the GitLab API. Supports complete merge request analysis, file-specific reviews, and version comparisons.

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 comprehensive Chef-to-Ansible migration for enterprise infrastructure with 34 tools across cookbook analysis, resource conversion, InSpec integration, secrets management, and AWX/AAP deployment configuration.

A production-ready MCP server template that enables developers to quickly build and deploy MCP servers with dynamic tool/resource loading, YAML-based prompts, and seamless OpenShift deployment. Supports both local development with hot-reload and production HTTP deployment with optional JWT authentication.

Enables interaction with Gerrit code review system via REST API, allowing language models to query changes, retrieve details, and manage reviews.

Enables orchestrating secure software development pipelines with domain-specific compliance (HIPAA, PCI-DSS, etc.), generating pseudocode, threat models, and CI/CD from user stories via natural language.

Enables programmatic management of Koyeb resources including apps, services, deployments, and instances via the Model Context Protocol. It allows users to query build and runtime logs and perform resource lifecycle operations through the Koyeb API.

MCP server for GitLab that provides 85 tools covering issues, merge requests, repositories, CI/CD, wiki, packages, and attachments, with support for both gitlab.com and self-hosted instances. It can read attachments so models can see screenshots and other files.

Unified capability catalogue with formally verified cartridges, distributed community hosting, and the Teranga menu system for single gateway access to a vast rabbfe of MCP, LSP, NeSy and related servers.!

Exposes all Dokploy API endpoints as MCP tools, enabling AI models and other clients to manage projects, applications, databases, and more on a Dokploy server.

Enables AI assistants to manage Prismatic integrations, components, and flows via the Model Context Protocol, supporting code-native integration development and component lifecycle.