MCP Servers Scratch

Overview

The Mcp Servers Scratch project implements a lightweight MCP (Model Context Protocol) server that exposes a minimal yet extensible set of capabilities for AI assistants such as Claude. Its primary goal is to provide a sandboxed environment where developers can prototype and test custom tools, prompts, and sampling strategies without the overhead of a full‑fledged production deployment. By exposing resources and tools through a standard MCP interface, the server allows AI clients to discover, invoke, and chain external functionality in a consistent manner.

This MCP server solves the problem of fragmented tool integration. In many AI workflows, developers must manually craft adapters for each external service—whether it’s a database query, an API call, or a custom script. Mcp Servers Scratch standardizes these adapters behind the MCP contract, enabling seamless discovery and invocation. The server automatically registers resources (e.g., data tables or files) and tools (e.g., transformation functions) so that an AI assistant can list available actions, request specific operations, and receive structured responses. This reduces boilerplate code and accelerates the iteration cycle when building assistant‑powered applications.

Key features of the server include:

• Dynamic Resource Registry – Exposes arbitrary data sets as MCP resources, allowing AI agents to query or manipulate them on demand.
• Tool Exposure – Wraps user‑defined functions as callable tools, complete with input validation and output serialization.
• Prompt Management – Hosts reusable prompt templates that can be fetched, parameterized, and injected into the assistant’s context.
• Sampling Control – Provides basic sampling parameters (temperature, top‑k) that the client can adjust to influence generation diversity.

Typical use cases span from rapid prototyping of conversational agents that need to access local files or databases, to testing new sampling strategies before committing them to a production MCP deployment. For example, a data scientist can expose a CSV file as a resource and a cleaning function as a tool; the assistant can then query the data, apply transformations, and generate insights—all within the same interaction. Similarly, a software engineer can expose an internal REST API as a tool and let the assistant orchestrate complex request flows.

Integration with AI workflows is straightforward: the MCP client—such as Claude’s built‑in connector—queries the server’s endpoint to discover available resources and tools. It then sends structured requests (JSON) to invoke a tool or retrieve data, receiving back typed responses that the assistant can incorporate into its next prompt. Because the server adheres to the MCP specification, it can be swapped out or extended without changing client logic, offering a plug‑and‑play advantage over custom adapters.

In short, Mcp Servers Scratch provides a developer‑friendly playground for experimenting with MCP features. Its minimal footprint, dynamic registration, and adherence to the protocol make it an ideal starting point for building robust AI‑powered applications that rely on external data and logic.