home / mcp / mcp server neurolorap

MCP Server Neurolorap

Provides code analysis and documentation tools to analyze projects and generate Markdown reports.

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "aindreyway-mcp-server-neurolora-p": {
      "command": "python",
      "args": [
        "-m",
        "mcp_server_neurolorap"
      ]
    }
  }
}

MCP Server Neurolorap provides integrated tools for code analysis and documentation generation, enabling you to collect code, analyze project structure, and generate Markdown reports that help you understand and document large codebases quickly.

How to use

You interact with the MCP server through an MCP client to run its tools. Use the code collection tool to gather code from a project or specific paths and export it in Markdown with syntax highlighting. Use the project structure reporter to analyze a project and generate a detailed Markdown report showing file sizes, complexity indicators, and recommended organization improvements.

How to install

Prerequisites: you need UV build tooling and a Python environment available on your machine.

Install using the preferred installer or package manager.

Install with UVx (recommended) and run the MCP server locally:

uvx mcp-server-neurolorap

Install with Python's pip (alternative):

pip install mcp-server-neurolorap

Configuration and usage notes

Run the server in normal mode to analyze and document code without developer tools, or start in developer mode to access a JSON-RPC terminal for direct interaction.

# Normal mode
python -m mcp_server_neurolorap

# Developer mode
python -m mcp_server_neurolorap --dev

Tools and outputs

Two primary MCP tools are exposed by Neurolorap. Use the code collector to gather code and generate a Markdown file, and use the project structure reporter to create a Markdown project report. You can run these tools via their respective interfaces in your MCP client.

Additional notes

Generated files follow a structured storage approach. Outputs are stored in a dedicated project data directory, and a symlink is created in your project root to expose generated artifacts for easy access in IDEs and file explorers.

Customize ignore patterns by creating a .neuroloraignore file in your project root. If absent, a default ignore file is created with common patterns.

Available tools

code_collector

Code collection tool that gathers code from an entire project, selective directories or files, and outputs a Markdown document with syntax highlighting and a table of contents.

project_structure_reporter

Project structure reporter tool that analyzes the project, generates a Markdown report, includes file size and complexity insights, and offers recommendations for code organization.