Brunossantana POC MCP Server

The Brunossantana POC MCP Server is a lightweight, proof‑of‑concept implementation of the Model Context Protocol that exposes a curated set of data‑access tools for developers building AI assistants. It focuses on three core domains—Loomers, Forms, and Projects—and provides a unified API that can be consumed directly by an AI client such as Claude. By offering ready‑made tools for querying and filtering these entities, the server eliminates the need for custom data‑fetching logic in each assistant project and accelerates prototype development.

At its heart, the server implements five primary tools: , , , , and . Each tool supports optional pagination, filtering, and sorting parameters, allowing AI assistants to retrieve precisely the slice of data they need. For example, a user could ask an assistant to list all Loomers located within a specified geographic region or to fetch the most recent form responses for a particular project. The tools are designed to be composable; an assistant can chain multiple calls—such as retrieving projects first, then fetching associated form responses—to build richer conversational experiences.

The value for developers lies in the abstraction layer that MCP provides. Instead of writing bespoke database queries or REST clients, a developer can expose a single MCP endpoint and let the AI client declare what data it requires. The server’s built‑in pagination and filtering reduce bandwidth consumption and improve response times, which is critical for real‑time interactions. Moreover, because the server adheres to MCP standards, it can be swapped out or upgraded without breaking existing assistants; only the tool definitions need to be updated, not the client logic.

Real‑world use cases include internal knowledge bases for project management tools, interactive dashboards that surface Loomer metrics to stakeholders, and automated compliance checks that scan form responses for required fields. In a customer support scenario, an assistant could pull the relevant Loomer and project data to answer queries about status or availability. In educational settings, instructors might use the form response tool to aggregate student feedback in real time.

The server’s architecture is intentionally simple yet extensible. It is built on Node.js 18+ with a modular folder layout: configuration, tool implementations, and type definitions are neatly separated. This structure makes it straightforward to add new domains or extend existing tools—for instance, by introducing a tool for project workflows—without disrupting the core MCP contract. The result is a scalable foundation that can grow alongside an organization’s data ecosystem while keeping AI integrations lean and maintainable.