MCP Servers

Overview

MCP Servers is a curated set of Model Context Protocol (MCP) implementations written in Swift, leveraging the SwiftMCP framework. By packaging these servers into a single repository, developers gain immediate access to ready‑to‑run MCP services that can be integrated with any AI assistant capable of communicating over the protocol. The primary goal is to lower the barrier for adding external data, tools, or custom prompts into an assistant’s workflow without needing to build the underlying protocol stack from scratch.

The servers expose a uniform API surface that mirrors the MCP specification: resources, tools, prompts, and sampling endpoints. Each server can be configured to provide static or dynamic content, allowing assistants to fetch knowledge bases, execute domain‑specific commands, or retrieve context from external services. For example, a server could expose a set of curated medical guidelines as resources while also offering a tool that queries an internal patient database. This dual capability lets the assistant answer complex questions with both reference material and real‑time data.

Key features include:

• Swift-based implementation: SwiftMCP brings type safety, performance, and native integration with Apple platforms, making it ideal for iOS/macOS developers.
• Modular design: Each server can be swapped or extended independently, encouraging reuse across projects.
• Full protocol compliance: Tested against popular MCP clients such as MCP Inspector and Claude for Desktop, ensuring compatibility with a wide range of assistants.
• Custom prompt handling: Servers can supply context‑specific prompts that guide the assistant’s generation, improving relevance and reducing hallucinations.

Typical use cases span from internal tooling to consumer products. A company could deploy an MCP server that interfaces with its proprietary knowledge base, allowing Claude or any other assistant to answer policy questions on demand. In education, a server might expose lesson plans and assessment tools, enabling an AI tutor to pull in the latest curriculum data. Even hobbyists can experiment with custom sampling strategies or tool chains without learning the low‑level MCP details.

By integrating these servers into an AI workflow, developers can rapidly prototype sophisticated assistant behaviors: fetch a document, run a calculation tool, and generate an answer—all orchestrated through MCP. The result is a modular, extensible architecture that keeps the assistant’s core logic separate from external data sources, simplifying maintenance and fostering collaboration across teams.