"Microsoft Certified Professional (MCP) and Microsoft SQL Server (MSSQL)" matching MCP servers:

Provides Azure DevOps tooling for AI agents, enabling interaction with projects, work items, repositories, and pipelines through natural language.

Enables static, database-free SQL health checks that return rule-based findings and verdicts, propose executable fixes, and verify rewrites are equivalent while comparing execution plans and suggesting indexes. Supports batch/file/git-diff review for CI gating, all at zero-dependency Tier0.

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.

Enables coding agents and CI/CD pipelines to audit untrusted code by detecting Python sandbox escapes, SQL injection taint, ReDoS regex vulnerabilities, and CORS misconfigurations, while generating Seccomp BPF filters for safer execution.

Enables SQL querying, schema browsing, query history, and human-approved change-plan workflows against Bytebase via the Connect-RPC API, secured with OAuth 2.1 PKCE and multi-process-safe token rotation.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

Agentic data quality MCP server — runs structured validation rules against warehouses (DuckDB, BigQuery, Athena, Databricks, Postgres), diagnoses failures with LLM root cause analysis, and proposes SQL remediations. Full audit trail of every AI decision.

MCP server for Sqemo, a web-based ERD tool with team naming standards. 34 tools: read/edit entities, attributes, and relationships; generate physical names from a shared glossary and naming rules; SQL (7 dialects) and DBML import/export; ERD lint, diff, and ALTER generation.

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.

QueryPilot enables AI agents to safely generate, validate, repair, and execute SQL against fixture databases, with eval-driven regression testing to ensure reliability.

Query Prometheus from CLI or any MCP client, with retries, caching, and an optional metrics exporter.

A boilerplate for building FastMCP servers, providing a basic setup for testing, linting, formatting, and publishing to NPM.

Generate professional changelogs from git history and suggest semver bumps, with optional AI enhancement via Claude.

Provides Kubernetes deployment intelligence with typed query tools and AI-synthesized risk briefs, enabling users to list workloads, get detailed snapshots, query Prometheus metrics, record deployment history, and generate risk assessments before promoting to production.

Enables AI assistants to interact with Woodpecker CI to debug pipeline failures, analyze build logs, troubleshoot configurations, and perform pipeline and repository actions such as triggering, restarting, canceling, approving, activating, and updating settings.

A self-hosted remote MCP server for Azure DevOps that bridges OAuth 2.1 between MCP clients and Microsoft Entra ID, enabling any OAuth-capable client (e.g., Claude Code, Claude Desktop) to access Azure DevOps resources.

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

Interact with OneDev for CI/CD spec editing, pull request review, and issue workflow automation etc.

MCP server for deploying files via FTP and running SQL via phpMyAdmin on cdmon shared hosting, with safety features like path traversal protection and rate limiting.

A Model Context Protocol server implementation that allows AI models to interact with and manage Spinnaker deployments, pipelines, and applications through a standardized interface.

A production-ready template for building Model Context Protocol servers in Python, featuring example tools, structured logging, and multiple transport protocols.

Enables AI agents to query build/deploy status and request operations on a self-hosted Docker/Compose deployment platform, with Observer and Operator skills and approval-based write actions.

Enables running SQL warehouse validation checks through natural-language questions in MCP clients, while reusing the same logic for automated pipeline and CI/CD runs.

Provides access to PyTorch CI/CD analytics data including workflows, jobs, test runs, and log analysis through an MCP interface.

A basic MCP server template built with FastMCP framework that provides example tools for echoing messages and retrieving server information. Serves as a starting point for developing custom MCP servers with Docker support and CI/CD integration.

Provides AI assistants with access to Capawesome documentation and Cloud management tools, enabling app, build, and deployment operations via API token authentication.

Local MCP decision gate for AI agents and CI/CD: check candidate actions against owner-controlled policy and proposal-bound evidence. Three read-only tools report admissible options, missing evidence, and SHA-256 receipts. No selection, execution, or DeltaX API key.

Mneme exposes a governed Decision Index so MCP clients can propose candidate architectural decisions and query decision state, applicability, and traceability. Proposals remain non-authoritative: accepting or activating protections stays under explicit human control.