"AI Tools for Assisting Architectural Engineers with Engineering Tasks" matching MCP servers:

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.

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.

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 dynamic MCP server that automatically discovers Python files in a directory and exposes them as tools to any MCP-compatible AI client, allowing users to easily create and deploy custom AI tools.

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.

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.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Enables AI assistants to perform controlled Linux system administration tasks like reading logs, managing services, cron jobs, WordPress, and executing sandboxed Python code, with strict security constraints.

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.

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

A minimal MCP server that exposes a run_command tool over stdio, enabling command execution within a Docker container.

Enables Claude Agent SDK agents to safely operate a remote user machine via a WebSocket tunnel, providing six client__* MCP tools with path fencing and phase-switch adjudication.

Enables coding agents to perform file, search, patch, git, process, test, package, network, and system operations through 60 typed MCP tools with structured inputs/outputs, structured errors, and a full event journal, replacing terminal use with a typed machine API.

Empower any MCP-compatible AI Agent(MCP Client) with engineering-grade capabilities to understand, modify, run, and deliver real-world code repositories.

A language-neutral runtime for the SEP-2663 task lifecycle and io.sdar/taskExecution Provider Profile, delegating resource facts and side effects to versioned gRPC/Protobuf adapters. Implemented in strict TypeScript, it provides durable scheduling, recovery, and a full test suite for conformance.

Enables AI agents to execute tools securely with Docker isolation, tier-based permissions, and feature flags, providing a centralized tool registry and MCP server.

A Model Context Protocol (MCP) server that provides TypeScript development tools for automated refactoring and code analysis.

Extends VS Code Copilot with MCP tools for persistent semantic memory, code indexing and RAG search, sandboxed Python/shell execution, web fetch/search, dynamic tool management via a web UI, and structured multi-step reasoning.