"Overview and Information on OpenAPI Specification" matching MCP servers:

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 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 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.

Agentic MCP server that receives a feature specification from an IDE, autonomously implements it in an isolated sandbox via an internal loop (plan-act-validate-correct), and returns a structured report with diffs and test results, without modifying the original repository.

An MCP server for Trae AI that enables automated creation and management of OpenSpec-compliant projects through natural language descriptions. It features tools for generating specification files, optimizing existing code based on specs, and deploying project templates without command-line intervention.

An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.

Enables agents to generate typed client code from OpenAPI, GraphQL, Protobuf, and SQL specs using MetaEngine's code-generation platform.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

Enables multiple AI coding agents to collaborate on a project by coordinating tasks, file leases, and messages through a shared hub, preventing conflicts and enabling parallel development.

Provides a specification-driven workflow layer for AI-assisted coding, enabling agents to follow an explicit 11-phase feature workflow with checkpoints, artifacts, and quality gates.

Enables users to run shell commands with compressed output, extract information from large files, drive local opencode and Codex agents, perform browser-automation tasks, and monitor runs via a live dashboard.

Integrates Nautex AI's specification and planning tool with coding agents to manage requirements, tasks, and code generation in small, testable steps.

A zero-dependency MCP server that allows multiple coding agents to coordinate work on the same repository using file locks, task claims, and status messages.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.

Enables running code generation, deterministic compiler validation, sandboxed tests, and contract checks entirely on local hardware, with autonomous self-healing loops and compact telemetry receipts for orchestrator models.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.

Your AI coding agent tests pages, copy, and flows on simulated users while building.

An MCP server that lets an AI coding agent delegate implementation work to the OpenCode CLI, choosing an explicit provider + model on every call.