"Building and Deploying a React Application with Docker and a Postgres Backend" matching MCP servers:

MCP server for the ReactUse library. Exposes 110+ React Hook signatures, examples, and API metadata so AI coding assistants can discover and use any hook from @reactuses/core directly.

A production-grade TypeScript starter for building Model Context Protocol servers, supporting stdio and Streamable HTTP transports with modular tools, resources, and prompts.

Provides access to PatternFly React documentation, development rules, and component schemas through MCP tools, enabling AI assistants to help developers build applications with PatternFly components following best practices.

Provides an institutional research backend for AI assistants, with 15 tools for company, financial, funding, competitor, industry, and news intelligence, plus Markdown/PDF report generation, featuring deterministic source routing, extraction, validation, and citation generation.

A production-grade, extensible Python template for building Model Context Protocol servers with support for Streamable HTTP and stdio transports. It provides a structured framework for implementing tools, resources, and prompts with built-in authentication, observability, and background task management.

MCP server for Argo CD that provides multi-environment profiles, SSO or API key authentication, application search with cache, and REST API tools from the bundled OpenAPI catalog.

Enables LLMs to access MCP protocol specifications and TypeScript SDK documentation with context-aware search and code templates for building custom MCP servers.

A minimal TypeScript starter template for building Model Context Protocol (MCP) servers with auto-loading architecture for tools, resources, and prompts. Includes code generators, dual transport support (stdio/HTTP), and production-ready structure.

A universal MCP server enabling AI assistants to query and manage six database engines (Postgres, Redis, Elasticsearch, MySQL, MongoDB, LDAP) through 113+ tools with read-only safety and fault tolerance.

Integrates GlitchTip error monitoring with AI assistants to fetch, analyze, and debug production errors. It enables users to list issues, retrieve event details, and perform guided triage of application errors through natural language.

A flexible proxy server that aggregates multiple backend MCP servers into a single interface using STDIO or SSE transports. It supports dynamic server management via an HTTP API and utilizes namespacing to prevent tool conflicts across connected services.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

Local MCP server for generating image assets with Google Gemini (Nano Banana 2 / Pro). Supports transparent PNG/WebP output, exact resizing/cropping, up to 14 reference images, and Google Search grounding.

A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.

Enables AI assistants to manage WordPress networks through a MainWP Dashboard, including site management, updates, plugin/theme control, and bulk operations via natural language.

Enables AI assistants to search live jobs, view full details, save and track applications in a Kanban board, set follow-up reminders, and subscribe to job alerts—no account needed for searching.

Operations toolchain for SQLite, Cloudflare D1, and Turso. Every command is available as both a CLI and an MCP tool: inspect, diff, migrate, monitor, back up, and repair databases. 22 stdio tools, read-only by default.

Enables Yelp business search, place details, and full review feeds as structured JSON through a hosted MCP endpoint, with no Yelp Fusion key required.

Enables fast literal and regex search over local workspaces using a sparse n-gram index for candidate pruning, with deterministic verification against file contents.

An MCP server that turns AI assistants into competent Festo/CODESYS PLC engineers by grounding them in curated, manufacturer-accurate knowledge and forcing every artifact through a machine-checked validation pipeline for CODESYS import.