"MCP servers for full-stack development, DevOps, Supabase, QA, and AI agent orchestration" matching MCP servers:

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

Connects to Stack AI workflows, allowing you to run workflows and pass user inputs through MCP-compatible clients.

Provides a local groundedness verifier as an MCP server, returning a verdict on whether a draft answer is supported by provided document excerpts.

Enables users to connect and run Stack AI workflows directly through MCP-compatible clients. It allows for passing user inputs to specific projects and receiving structured responses from deployed AI applications.

Enables Cursor and Windsurf to safely interact with Supabase databases by providing tools for database management, SQL query execution, and Supabase Management API access with built-in safety controls.

Enables secure interaction with Supabase databases via the PostgREST API, supporting full CRUD operations and stored procedure calls. It utilizes the Dedalus MCP framework to provide encrypted credential handling and just-in-time token exchange.

Enables intelligent Kubernetes deployments, automated documentation testing, organizational pattern management, and shared prompt libraries. Provides AI-powered recommendations based on cluster capabilities and automates DevOps workflows through conversational interfaces.

Enables minimal interaction with Supabase databases through 4 essential tools for data querying, mutations, file storage, and user authentication. Designed for 70% less context usage than standard implementations with auto-truncated results and simplified parameters.

MCP server for Supabase instances running on Aliyun RDS, enabling AI assistants to interact with your Supabase instance hosted on Aliyun cloud infrastructure.

WordPress plugin that runs an MCP server on your own site: search and read published posts and pages, look up WooCommerce products, describe the site as schema.org types and query site figures, with drafts only when the site enables them. Signed-in access by default; anonymous access and OAuth are optional and off.

Crawls and indexes documentation websites to Supabase with vector embeddings for RAG, using smart sitemap discovery and Jina AI for fast content extraction with multi-project support.

Enables AI assistants to handle customer support by looking up order status, semantically searching knowledge bases, and creating support tickets through MCP tools, with grounded answers and automated evaluations.

Enables natural language question answering over academic PDFs with grounded answers, exact source attribution, and explicit uncertainty handling.

Enables AI agents to execute any SQL command (including DDL/DML) on a PostgreSQL database with zero restrictions, allowing full database control and dynamic database switching.

Enables AI agents to interact with any website through MCP, providing structured knowledge graph contexts, generated actions, and readiness scoring.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

A local MCP server that answers natural language questions over a collection of research PDFs, using semantic retrieval and Gemini for grounded answers with citations.

Provides MCP tools to query device status, customer data, orders, and open incidents, and draft incident notes using mock SAP data.

Orchestrates multiple AI agents to process complex queries by intelligently splitting tasks, executing them in parallel, and synthesizing results using local Ollama LLM inference.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.