Neurolorap MCP Server

The MCP Server Neurolorap is a purpose‑built assistant that bridges the gap between raw codebases and AI‑driven analysis. By exposing a set of specialized tools over the Model Context Protocol, it lets developers and AI assistants query project metadata, extract source files, and generate structured documentation without leaving their existing workflow. The server solves the common pain of manually sifting through thousands of files to understand a codebase’s layout or to produce up‑to‑date documentation, which is especially valuable when integrating large language models into continuous integration pipelines or code review bots.

At its core, the server offers two high‑level capabilities. First, the Code Collection Tool traverses a project tree, harvesting source files from entire directories or selected paths. It outputs richly formatted Markdown that includes syntax highlighting and an automatically generated table of contents, supporting multiple languages out of the box. Second, the Project Structure Reporter Tool performs a quantitative analysis of the repository, calculating file sizes, complexity metrics, and producing a tree‑based visual report. It can also suggest refactorings or organization improvements based on customizable ignore patterns, making it a practical companion for code quality gates.

Developers benefit from the server’s tight integration with Cline and its lightweight command‑line interface. A single invocation spins up the service, installing dependencies and registering tools with the MCP ecosystem. Once running, AI assistants can invoke either tool via simple JSON‑RPC calls, receiving ready‑to‑use Markdown artifacts that can be injected into documentation sites, Slack channels, or GitHub PR comments. The developer mode further provides a JSON‑RPC terminal for exploratory use, allowing rapid prototyping without writing any code.

Real‑world scenarios include automated documentation generation for open‑source projects, pre‑commit hooks that flag overly large modules, or AI‑driven code review assistants that surface project structure insights in the context of a pull request. Because the server’s outputs are Markdown‑based, they integrate seamlessly with static site generators and documentation frameworks like MkDocs or Docusaurus. The combination of automated code harvesting, structured reporting, and protocol‑level extensibility gives the Neurolorap MCP server a distinctive edge for teams that need consistent, machine‑readable insights into their codebases.