"Hybrid Memory Models Combining Relational, Graph, and RAG Approaches" matching MCP servers:

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.

This MCP server enables intelligent API testing automation by combining RAG knowledge retrieval with tool execution capabilities. It allows QA engineers to perform natural language-driven API testing with contextual knowledge support.

Post-quantum readiness for AI coding agents: scans code for quantum-vulnerable cryptography (RSA/ECDH/ECDSA/DH) and returns NIST ML-KEM/ML-DSA/SLH-DSA (and hybrid) migration guidance, with fix verification and dependency checks. Content-based, advisory tools only; runs local (npx @quantakrypto/mcp) or as a hosted OAuth endpoint.

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.

Local MCP runtime that gives persistent cross-session memory to 12 AI coding agents (Claude Code, Cursor, Codex, Gemini CLI, Windsurf, Amp, and more). SQLite-backed state survives context resets. Install: npm install -g @egchq/egc

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.

A local-first AWS security tool that uses graph theory to discover attack paths (e.g., Internet → Role → DB) and prioritize remediations. It allows agents to perform read-only security audits and generate Terraform fixes without data exfiltration.

MCP-native release confidence control plane that turns browser execution into auditable go/no-go decisions by combining business-critical journey context, evidence-heavy QA runs, and risk governance.

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 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.

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.

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 agents to deploy production APIs from JSON schemas, with 44 tools for managing projects, schemas, deployments, and graph data.

Give your coding agent the dependency graph it is about to change: scan, diff, explain, audit.

Enables AI models to interact programmatically with Dokploy servers to manage projects, applications, and deployment services. It provides 67 specialized tools for direct Dokploy API operations through the Model Context Protocol.

Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.

A production-ready template for building Model Context Protocol (MCP) servers in Python, using Docker Compose for containerized development and CI/CD.

Enables comprehensive DevOps infrastructure management through tools for Jenkins, Ansible, Terraform, Kubernetes, and Docker operations. Features a sophisticated memory system for context retention and provides validation, generation, and optimization capabilities across DevOps workflows.