MCPHubs

Overview of MCPHubs

MCPHubs is a purpose‑built Model Context Protocol (MCP) project discovery and management platform that streamlines the process of finding, evaluating, and integrating open‑source MCP projects into AI assistant workflows. By harvesting data from GitHub, applying AI‑driven relevance scoring with Google Vertex AI’s Gemini Pro model, and serving the results through a fast, cache‑optimized API, MCPHubs solves the common pain point of manually hunting for suitable MCP extensions and ensuring they remain up‑to‑date.

Developers building AI assistants often need to plug in external tools, data connectors, or prompt templates that conform to MCP. The platform’s intelligent search engine—supporting six distinct GitHub query strategies—automatically surfaces over 200 MCP‑related repositories. Each candidate is scored for relevance, completeness, and community activity, so teams can quickly prioritize high‑quality resources. The daily automated sync at 6 AM guarantees that the catalog reflects the latest commits, while a multi‑tier caching system drops response times from five seconds to under half a second.

Key capabilities include:

• AI‑powered relevance analysis that filters noise and highlights truly useful MCP projects.
• Real‑time API endpoints (, ) that expose the curated list for integration into assistant toolchains.
• Admin console for manual sync triggers, health checks, and detailed operation logs—critical for maintaining production‑grade reliability.
• Supabase backed persistence with PostgreSQL, providing durable storage and scalable query performance.
• Enterprise‑grade security: API key protection for sync operations, automated environment variable scanning, and strict role‑based access via NextAuth.js.

Typical use cases involve adding new data connectors to a Claude or other MCP‑compatible assistant, automatically populating a tool library in a low‑code platform, or conducting an audit of third‑party MCP extensions for compliance. Because the platform exposes a clean REST interface and is built on Next.js 15 with TypeScript, it can be dropped into existing CI/CD pipelines or serverless deployments (Vercel, Fly.io) with minimal friction.

In short, MCPHubs gives AI developers a single source of truth for MCP projects, backed by AI‑enhanced discovery and enterprise‑grade performance, thereby accelerating the integration cycle from idea to production.