"Resources and Information on Mechanical Engineering" 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.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

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

Enables users to generate complete, production-ready software projects from simple ideas by coordinating 8 specialized AI agents through the Model Context Protocol.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.

Enables reproducible evaluation of AI coding agents by exposing repository inspection, code editing, test running, and deterministic verification through MCP tools.

a claude code harness engineering tool

Exposes 31 high-assurance agentic coding skills as MCP tools and resources, enabling AI agents to invoke structured workflows across DISCIPLINE, TECHNIQUE, KNOWLEDGE LAYER, and REFERENCE registers.

Enables AI agents to perform software engineering tasks inside an isolated, deterministic sandbox—exploring repositories, reproducing failures, applying patches, running tests, and verifying solutions against hidden suites through MCP tools.

A single, queryable source of engineering constraints that coding agents consult at code-generation time, exposed over an MCP server.

Enables Claude Code to delegate software-engineering tasks to OpenCode agents working in the same workspace.

Enables coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

An MCP server that connects Gemini 2.5 Pro to Claude Code, enabling users to generate detailed implementation plans based on their codebase and receive feedback on code changes.

Orchestrates multiple AI coding agents declaratively to automate software development workflows for engineering teams.

A local MCP server that lets Claude delegate scoped work to Codex with structured results and guardrails, supporting planning, code review, build, reverse engineering, and long-running background tasks.

NEXUS enhances AI coding agents with semantic code indexing, context engineering, change impact analysis, persistent memory, safe execution, and deterministic orchestration through 31 MCP tools.

Enables MCP-compatible clients to delegate senior software engineering tasks like code generation, review, Jira management, and architecture recommendations to a model with a senior engineer persona.

Dynamic context and active engineering workflow for AI coding assistants, enabling project-aware rules, code review, TDD, and delivery orchestration.

MCP server providing persistent engineering memory and spec-driven development workflows for AI coding agents, preserving learnings across sessions.