RR MCP Server for .NET Analysis

Overview

The RR MCP Server for .NET is a lightweight, cross‑platform tool that exposes the internal structure of any .NET solution to AI assistants via the Model Context Protocol. By parsing a solution file with PowerShell scripts, it produces machine‑readable representations of models, entities, enums and interfaces. This enables AI agents to understand the project’s domain model without having to read or expose source code, thereby preserving confidentiality while still providing rich context for generation and analysis.

The server solves the common developer pain point of “knowledge transfer” between a large codebase and an AI assistant. Instead of manually documenting or exposing the entire solution, RR MCP automatically extracts the relevant types and contracts, caches them for fast repeat queries, and returns clean JSON objects that are easy to consume. The output is free of console noise, fully logged for troubleshooting, and can be warmed up or debugged by running the underlying PowerShell scripts directly.

Key capabilities include:

• Data extraction: Enumerates all classes ending in *Model, *Entity or *Type, capturing properties, base types and project namespaces.
• Interface extraction: Retrieves interfaces that follow typical naming patterns (IService, IRepository), including documentation and method signatures.
• Caching & performance: Stores results in a directory, eliminating repeated parsing of the same solution.
• Cross‑platform support: Runs on Windows, macOS and Linux with PowerShell 7+.
• Seamless MCP integration: Auto‑registers tools such as and , making them immediately available to any MCP‑compatible client (Cursor, Copilot, Claude, etc.).

Typical use cases are:

• AI‑driven code completion: An assistant can ask for the signature of an interface method or the shape of a data model before generating implementation code, ensuring consistency with existing architecture.
• Documentation generation: Tools can pull interface contracts and data structures to auto‑generate API docs or client SDKs.
• Automated refactoring: An AI can analyze the current model hierarchy, suggest changes and apply them with confidence that it understands the entire type graph.
• Security compliance: By exposing only the abstracted JSON representation, sensitive source code remains on disk while still enabling AI analysis.

In practice, a developer configures their MCP client to point to the RR MCP executable. The assistant then calls or , passing a solution path, and receives structured context that can be combined with other prompts or tools. This tight integration streamlines AI workflows, reduces manual effort, and keeps the codebase secure while unlocking powerful generative capabilities.