MCP Client And Server From Scratch

Overview

The MCP Client And Server From Scratch project tackles a common pain point for developers building AI‑enabled applications: the lack of a lightweight, self‑contained Model Context Protocol (MCP) implementation that can be embedded into existing codebases without heavy dependencies. By providing both client and server components written from the ground up, this MCP solution removes the need to rely on external libraries or cloud‑hosted services. Developers can therefore ship fully local AI assistants that respect privacy, reduce latency, and give them full control over the tools and resources exposed to the model.

At its core, the server implements the MCP specification’s core concepts—resources, tools, prompts, and sampling—in a minimalistic yet extensible architecture. Resources are arbitrary data objects that the model can reference; tools are executable actions (e.g., HTTP requests, database queries) wrapped in a consistent interface. Prompts are templated instructions that guide the model’s behavior, while sampling controls how the model generates text (temperature, top‑k, etc.). By exposing these constructs through a simple HTTP/JSON API, the server lets any MCP‑compatible client (Claude, Gemini, etc.) discover and invoke them in real time.

Key capabilities include:

• Dynamic tool registration – developers can add new tools at runtime, allowing the assistant to grow alongside application requirements.
• Fine‑grained prompt management – prompts can be stored, versioned, and selected programmatically, enabling consistent model behavior across sessions.
• Custom sampling controls – the server accepts per‑request sampling parameters, giving developers instant access to experimental or production‑ready generation settings.
• Resource sharing – large data artifacts (e.g., knowledge graphs) can be loaded once and referenced by multiple requests, improving efficiency.

Typical use cases span from internal business bots that need to query proprietary databases and generate reports, to educational platforms where students interact with a tutoring assistant that can fetch up‑to‑date course materials. Because the server is self‑contained, it fits neatly into CI/CD pipelines or containerized deployments, making it ideal for regulated industries that require on‑premises AI solutions.

Integration into existing workflows is straightforward: a developer writes a thin MCP client that points to the local server, then configures their AI assistant’s tool list to include the custom endpoints. The MCP client handles authentication, request formatting, and response parsing, so the assistant’s code remains agnostic of the underlying implementation. This modularity not only speeds up development but also ensures that future changes to tools or prompts do not ripple through the assistant’s core logic.