"Resources for Learning Reverse Engineering Skills" matching MCP servers:

A Model Context Protocol server that enables Claude to execute Python code using boto3 to query and manage AWS resources directly from conversations.

A local STDIO MCP server that bridges MCP clients to the Codex CLI by sending instructions to a configured workspace, exposing task run, status, and result tools with a read-only sandbox and no remote transport.

Enables running Claude Skills locally on your Mac with sandboxed execution, supporting document processing and integration with MCP-compatible AI tools while ensuring data privacy.

MCP server that turns coding workflows into explicit execution graphs (DAG) with deterministic command execution, human-in-the-loop gates, and tamper-proof run history for Claude Code.

Enables ChatGPT to interact with Engineering MCP through a secure MCP endpoint, including executing C2C plans and workspace file access with operator approval.

Exposes the automated feature engineering LangGraph agent as MCP tools, enabling dataset profiling, feature planning, and feature code execution through MCP-compatible hosts.

Enables AI agents to safely inspect, edit, and test code within a bounded repository environment to solve software engineering tasks and verify fixes.

Manages a Neo4j knowledge graph of reusable Python functions, documentation, and environment variables, enabling dynamic script composition and execution with automatic dependency management and secret protection.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A production-ready MCP server that equips AI agents with dynamic, persistent, and executable skills. It enables secure script execution and progressive disclosure to reduce context window usage.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

A comprehensive learning project demonstrating MCP server implementation with tools for calculator operations, file management, and system information retrieval, including Google ADK integration patterns and best practices.

Provides dynamic, context-aware code assistant skills through hybrid RAG (vector + knowledge graph), enabling runtime skill discovery, automatic toolchain-based recommendations, and on-demand loading from multiple git repositories.

An MCP server that transforms Claude-style skills and resources into callable tools for any MCP-compatible agent or client. It automatically discovers, exposes, and executes scripts from skills organized in local directories or packaged archives.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

A platform that transforms AI development with intelligent context management, optimization, and prompt engineering, enabling developers to enhance model performance through structured context management and optimization tools.

A comprehensive educational server demonstrating Model Context Protocol capabilities for tools, resources, and prompts, allowing AI assistants to connect to external data and functionality.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.