"LLVM" matching MCP servers:

MCP server that exposes an AI decompiler via an OpenAI-compatible API, enabling decompilation, explanation, and variable renaming of disassembly for binary analysis.

Bridges local LLMs running in LM Studio with MCP clients like Claude Desktop to perform reasoning and analysis tasks while keeping sensitive data private. It features a suite of tools for local code review, privacy scanning, and content transformation using auto-discovered local models.

MCP server exposing a shared pr-review skill with tools to list, get, run, and evaluate skills, enabling AI assistants to perform code review tasks.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

MCP server connecting Claude Code to LM Studio, delegating token-expensive tasks to a local model while keeping the cloud model in control. It reduces cloud context usage by reading files locally and returning only the processed results.

A privacy-first MCP server that provides local LLM-enhanced tools for code analysis, security scanning, and automated task execution using backends like Ollama and LM Studio. It enables symbol-aware code reviews and workspace exploration while ensuring that all code and analysis remain strictly on your local machine.

Enables coding agents to inspect Python applications, research source and target models, and produce reviewable migration plans with evidence gates and evaluation before production code changes.

MCP server for reviewing code changes using LLMs, supporting Copilot, Ollama, and OpenAI-compatible endpoints.

Processes large files, error logs, and codebase searches locally via Ollama to reduce token consumption in Cursor by up to 80%.

Integrates local language models (like Qwen3-8B) with MCP clients, providing tools for chat, code analysis, text generation, translation, and content summarization using your own hardware.

Provides Cursor-like code intelligence using tools like ripgrep, ctags, and tree-sitter to help LLMs explore and understand entire codebases. It implements a structured, phase-gated workflow to ensure high-confidence code modifications and eliminate hallucinations.