"CSS" matching MCP servers:

A local stdio MCP service that unifies coding agents like Codex and Claude into cs_agent_* tools, enabling the root agent to create, invoke, and manage child agents with recursive delegation.

Visual feedback for AI coding agents on Astro sites, providing live annotations with source file:line, CSS selector, Tailwind classes, and computed styles.

MCP server that parses .sketch design files into CSS-aligned, LLM-friendly JSON, enabling AI coding agents to generate high-fidelity HTML, Vue, or React code.

Enables Codex to delegate review and implementation tasks to the local Qoder CN CLI as background jobs, with two permission tiers and polling-based status updates.

Exposes the local Claude Code CLI as a subagent for Codex, enabling background review and implementation jobs with two fixed permission tiers.

A Model Context Protocol (MCP) gateway for running Claude Code, Codex, Gemini, Grok, and Mistral (Vibe) CLIs from one MCP endpoint, with durable async jobs, session continuity, cache-aware prompting, observability, and personal-appliance setup tooling. Why developers try it: one local MCP endpoint for cross-LLM validation, multi-agent coding workflows.

Enables an AI coding agent to work through a fixed seven-phase cycle with enforced reflection gates and artifacts instead of jumping straight to code. Supports persistent tiered project memory with confidence decay, task analysis and routing, multi-model answer merging, and pattern-based safety warnings, while keeping cycle state on disk so a new chat can resume where the last one stopped.

Enables ChatGPT clients to submit, monitor, and retrieve persistent, approval-aware local coding tasks on macOS, preserving state and results across disconnects through a local-only MCP bridge.

Gives any MCP client the same sandboxed coding-agent toolset — searching, viewing and editing files, running tests, and executing shell commands — each session in its own locked-down, network-isolated Docker container. Enables an MCP-capable assistant to diagnose and fix failing tests without touching the host machine.

Enables semantic code search across projects using AI embeddings to find code by meaning rather than just text matching. Provides fast intelligent search, symbol analysis, and code similarity detection with multi-language support.