"SearXNG, a privacy-respecting metasearch engine" matching MCP servers:

A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.

Turn the AI you already use into a game studio. Describe a game and Claude or Cursor builds it as a real 3D browser game: playable immediately, hosted on its own link, shareable with anyone. No engine, no build step, no code knowledge, nothing to export. Your AI ships the whole thing: it pulls free 3D assets, rigs characters, and playtests its own build until the game works. Free to start.

Provides coding agents with persistent, evidence-backed project memory and knowledge across sessions, using a structured memory tree and local knowledge base.

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

Enables AI assistants to create and edit 3D scenes, geometry, scripts, and builds in the Forma engine, with changes reflected live in the editor.

An authenticated OpenAI-compatible inference gateway with an async job broker that bridges a coding agent to a ChatGPT MCP worker, enabling ChatGPT to process inference requests via a tunnel.

A TypeScript MCP server that lets AI assistants interact with the Godot 4.x game engine: not just editing files, but playing the game.

A fully local, privacy-first MCP server that gives AI coding assistants deep repository intelligence with file-and-line-cited answers, persistent semantic memory, and agentic abilities like task planning and code review—all without any cloud API calls.

Connects AI assistants to a local Codex engine for performing deep, project-level code reviews and automated refactoring. It enables context-aware bug fixes and multi-file analysis through a standardized bridge between modern AI clients and local development environments.

Sovereign, deterministic, local-first context and DAG lifecycle engine with native stdio MCP server for autonomous AI coding agents.

A local project intelligence, batch coding, and self-learning engine for Codex that enables task-level batch coding, reduces token usage, and automatically learns skills.

Privacy-first, read-only repo intelligence for AI coding agents: scans local codebases and recommends MCP servers, repos, and research without sending data to the cloud.

Enables AI agents to build games with the Cocos4 engine by providing high-level scaffolding tools and transparently proxying native MCP tools.

A local-first MCP gateway that reduces context overhead in coding-agent sessions by exposing only relevant tools and enforcing task-appropriate engineering gates, with privacy-first defaults and no network calls until explicitly enabled.

MCP servers that let Claude Code delegate to other coding CLIs (Copilot, Codex, Gemini) for cross-model second opinions, structured reviews, and multi-engine consultations.

Wraps the Codex engine in a local stdio MCP server so any MCP-capable agent can run coding turns as tools, with blocking runs, start/poll progress, command approvals, interrupts, thread resume, and token usage. It also supports custom OpenAI-compatible providers, letting teams blocked by signup, payment, or network limits still use the engine.

AI-native open-source 2D game engine whose MCP server exposes every editor operation to coding agents as typed commands, so an agent can build, run, and verify a game inside the editor. It runs locally on macOS, Windows, and Linux with no cloud service or API key.

An autonomous MCP server for AI-powered software generation using Gemini and Groq, with features like dual-engine strategy, visual inspection, and QA sentinel.

Enables a persona-driven coding pair programmer to plan and execute code work through a full MCP tool loop, merging servers for image generation, live web search and fetch, a persistent shell with file access, audio/video processing, and hundreds of third-party app integrations like Gmail, Slack, GitHub, and Drive. Long conversations are automatically compressed to stay inside the engine's context window, tool calls are billed to the caller's own Pollen, and the whole agent runs on a single engine chosen because it can actually emit tool calls.

Plori runs AI agents in Workspaces. A Workspace is a project folder in the cloud. Several agents can work in it at the same time, each on its own copy of the files, and Plori merges their work back into the folder. Every version of the files is kept.