"How to query data from a Snowflake database" matching MCP servers:

An MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.

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.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

Enables developers to manage Genesys Cloud Function Data Actions through natural language in Cursor, including validating, building, planning, and applying function deployments while enforcing safety controls like plan-before-apply and read-only mode.

Publishes static sites to Drop2Run from an AI agent: hand it files written in the chat or a folder on disk, and it returns a live HTTPS URL. Six tools — publish_files, publish_dir, list_sites, delete_site, and login / login_code — with no git, no build step and no repository.

MCP aggregator with many existing NCP templates and allows running external MCP servers too

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.

Enables AI agents to deploy static files and get live HTTPS URLs instantly, with support for custom domains and data residency.

Enables coding agents to read Squally CI data, including test results, flakiness, and failure diagnostics.

Record an agent's MCP tool-call workflow once, replay it deterministically for zero tokens, and get a receipt — a snapshot-testing and deterministic-execution layer for Agent Skills.

Enables AI agents to retrieve the latest semantic version tags for container images from Docker Hub, GHCR, Quay, and other OCI registries, allowing them to pin specific versions for reproducible deployments.

Automates the full setup and deployment of Ballerina + Apache ActiveMQ integrations inside a WSO2 Integrator workspace, from broker validation to a runnable BI-low-code-ready project.

Stops your AI from re-introducing bugs, leaking provider keys, or weakening tests. Bug fixes become permanent regression guards; blocked mistakes become AI lessons the agent reads and learns from before its next edit.

Deploy, run and manage apps on your own self-hosted AppCrane from any MCP client. 62 tools: create an app, push code, deploy to sandbox, check health and logs, provision a managed Postgres, MariaDB, MongoDB or Redis, and promote to production after review. Your instance, your SSO, your data. Repo: https://github.com/gitayg/appcrane-mcp · npm: appcrane-mcp · Site: https://appcrane.dev

A Model Context Protocol server that monitors shadow-cljs builds and provides real-time build status updates for ClojureScript projects, allowing LLMs to verify build status after making code changes.

Deploys and manages projects on Swigs Cloud from any MCP client, turning folders or git repositories into live HTTPS sites in about two minutes.

A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, allowing users to query work items with plans to support creating/updating items, managing pipelines, handling pull requests, and administering sprints and branch policies.

Publishes normalized release notices to a configured webhook via MCP, with built-in validation, deterministic dry-run mode, and a clear boundary for authority gates.

Fetches the latest version numbers for popular GitHub Actions from simonw/actions-latest, allowing users to easily check and update workflow versions.

Analyzes database migrations for risks like lock contention and data loss before they run. Supports Flyway and Liquibase formats, providing risk scores and rollback generation.