MCP Registry Server

The Kbb99 MCP Registry Server is a lightweight, yet powerful registry that bridges AI assistants with the vast ecosystem of Model Context Protocol (MCP) services. In many modern AI workflows, developers need a quick way to discover and connect to the right tools—whether they are data pipelines, analytics services, or custom inference models. This server solves that problem by acting as a searchable index of MCP servers, allowing assistants like Claude to locate the most relevant service for a given task without manual configuration.

At its core, the registry offers a semantic search capability. By exposing the tool, an AI assistant can submit a natural‑language query and receive a ranked list of MCP servers that match the intent. This eliminates the need for hard‑coded URLs or manual lookup, enabling dynamic workflows where the assistant can adapt to new services as they are added to the registry. The server’s architecture is intentionally simple: it runs as a standalone Node.js process that can be launched from Claude Desktop or any other MCP‑compatible client, making it easy to integrate into existing development pipelines.

Key features include:

• Semantic Retrieval: Leverages modern NLP techniques to interpret user queries and surface the most relevant MCP servers.
• Tool‑Based Interface: The tool exposes a clean, single‑parameter API () that can be invoked by the assistant without needing to understand underlying search mechanics.
• Extensibility: Developers can add new MCP servers to the registry by publishing them through Smithery or directly via configuration, ensuring that the registry grows with the ecosystem.
• Open‑Source and MIT Licensed: The server’s permissive license encourages community contributions and easy incorporation into proprietary projects.

Real‑world scenarios where this registry shines include:

• Rapid Prototyping: A developer building a new chatbot can quickly discover available NLP or vision services, test them, and integrate the best fit.
• Continuous Integration: CI pipelines can query the registry to automatically select the most up‑to‑date version of a data‑processing MCP server before running tests.
• Multi‑Tenant Environments: In organizations with dozens of internal MCP services, the registry provides a single point of discovery that reduces configuration drift across teams.

By integrating this MCP Registry Server into an AI assistant’s workflow, developers gain a dynamic, searchable catalogue of tools that scales with their project needs. The result is faster iteration cycles, reduced configuration overhead, and a more resilient AI architecture that can adapt to new services as they emerge.