"Assistance with Developing Unity Games in C# and Debugging Code" matching MCP servers:

AI-powered static analysis for Unity projects, detecting common patterns, component relationships, and programming insights to augment developer workflows.

Provides AI assistants with structured access to Unity project metadata, build settings, and agent documentation directly from the filesystem. It enables querying project details and scene configurations without requiring the Unity Editor to be running.

Integrates AI assistants with the Unity Editor for scene manipulation, asset management, and testing, plus Unity documentation search via RAG.

Local MCP server that serves a local Unity documentation install to AI assistants, enabling full-text search and retrieval of Unity Manual and Script Reference pages through tools like search_unity_docs and get_page.

MCP server providing accurate Unity API documentation to prevent AI agents from hallucinating signatures, namespaces, and deprecated usage.

An MCP Server that provides Retrieval-Augmented Generation (RAG) capabilities to surface the latest Unity API documentation (currently v6.x).

Provides professional C++ code style checking, best practices analysis, and modern C++ upgrade suggestions with tools for naming conventions, memory safety, const correctness, and access to comprehensive C++ style documentation.

An MCP server that provides static validation and documentation lookup for quantum computing libraries, currently supporting PennyLane. It allows users to verify quantum code syntax and method usage against official library documentation to ensure correct implementation.

Provides AI agents with a governed, three-layer project memory (guide, code facts, and knowledge) through namespaced MCP tools for code search, context compilation, impact analysis, and proposal-driven documentation updates.

Provides access to comprehensive Riot Games API documentation for League of Legends, Teamfight Tactics, Valorant, and Legends of Runeterra, including endpoint details, parameters, and response formats.

Allows LLM tools like Claude Desktop and Cursor AI to access and summarize code files through a Model Context Protocol server, providing structured access to codebase content without manual copying.

Searches Claude Code documentation via BM25 indexing, returning ranked snippets with category filtering. Enables fast, local search over official docs without full-document scans.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Connects to vibe-code-best-practices.vercel.app to index 817 tools across 185 sections and 10 domains, routes the right tool/Skill for your project need, and can recommend a full stack.

Facilitates searching and accessing programming resources across platforms like Stack Overflow, MDN, GitHub, npm, and PyPI, aiding LLMs in finding code examples and documentation.

Provides AI assistants with access to personal best practice skills and coding guidelines stored as markdown files in a GitHub repository, enabling retrieval and listing of custom development standards.

An MCP server that performs code reviews by comparing local source code against design documents stored in Confluence. It integrates with Atlassian MCP servers to analyze documentation and provide suggestions for quality improvement based on design specifications.

Enables document-driven development by automatically detecting Markdown spec changes, generating implementation plans, and syncing code and tests through MCP tools.

Exposes a small catalog of programming study topics through the Model Context Protocol (MCP) for local learning.

Enables AI agents to manage, upload, chunk, and semantically search local documents through MCP tools and a REST API, with a built-in web dashboard and optional Gemini-powered AI search.