My MCP Server

Overview

The Alesion30 My MCP Server is a lightweight, custom‑built Model Context Protocol (MCP) server that bridges Claude Desktop with external data sources and services. By acting as a standardized “USB‑C port” for AI models, it lets developers expose arbitrary APIs or local databases as first‑class context providers. This solves the problem of siloed data: without MCP, an LLM must be hard‑coded to fetch information from a specific service or rely on external function calling, which can become brittle and hard to maintain. The MCP server introduces a clean separation between the AI client (Claude Desktop) and any number of back‑ends, enabling seamless data injection into prompts without changing the model’s internal logic.

At its core, the server implements three key MCP concepts: resources, tools, and sampling. Resources are static data endpoints that the LLM can reference directly; tools are executable actions (e.g., querying a weather API or running a local script) that the model can invoke on demand; and sampling allows the server to influence how Claude generates responses by providing additional contextual snippets. Together, these features give developers granular control over what information the model can access and how it is presented. For example, a developer could expose a local SQLite database as a resource, while also offering a weather‑forecast tool that retrieves real‑time data from an external API.

The server’s design makes it ideal for a wide range of real‑world scenarios. In customer support, an LLM can pull ticket history from a local database (resource) and call a ticket‑creation tool to open new issues automatically. In content creation, writers can query a knowledge base resource and use a summarization tool to generate concise outlines. Even in DevOps, the server can expose system metrics as resources and provide a rollback tool that executes scripts on demand. Because MCP is language‑agnostic, the same server can serve multiple AI assistants—Claude, GPT‑4, or any other MCP‑compatible client—without modification.

Integration into existing AI workflows is straightforward. Developers add the server’s executable path to Claude Desktop’s configuration, and the client automatically discovers available resources and tools. When a user asks a question that requires external data, Claude sends an MCP request to the server; the server responds with the requested context or executes a tool, and the result is injected back into the LLM’s prompt. This tight loop eliminates latency introduced by manual API calls and ensures that every piece of context is auditable and version‑controlled through the server’s own codebase.

What sets this MCP server apart is its extensibility and simplicity. It’s built with Node.js, making it easy to add new resources or tools using familiar JavaScript libraries. The server’s architecture encourages modularity: each resource or tool can be developed, tested, and deployed independently. Moreover, because MCP standardizes the interface, third‑party developers can create plug‑ins that drop into any existing MCP server without needing to rewrite the client side. This combination of flexibility, clear separation of concerns, and native support for Claude Desktop makes the Alesion30 My MCP Server a powerful addition to any AI‑centric development environment.