"Evaluating an Expo Go App and Associated React Native Code in an Android Emulator" matching MCP servers:

An MCP server that connects AI assistants to YNAB budgets, enabling natural language queries about finances backed by full API coverage and built-in YNAB methodology knowledge.

Generates React Native/Expo UI components using AI, integrates with Claude Desktop to create and optimize Tamagui-based components via natural language commands.

Enables AI agents to interact with the app via MCP tools and resources, supporting self-improvement through goal-setting and metrics tracking on Cloudflare Workers.

An MCP server that runs parallel AI agents to analyze React components for performance issues, including unnecessary re-renders, memoization errors, bundle size problems, and profiling concerns.

An MCP server for vibe coders to generate app icons with AI, auto-resize for iOS/Android, and publish to app stores, all from an AI coding assistant.

A Django-native MCP application and tool registration framework inspired by Celery, providing async tool definitions, autodiscovery, and stdio/Streamable HTTP serving built on the official MCP Python SDK.

Enables running ComfyUI native .app.json apps and managing the render queue through six single-purpose tools, with built-in UI-to-API conversion for headless or frontend ComfyUI and no panel plugin dependency.

Enables AI-powered video creation by letting the model write React/Remotion code, compiling it in real-time, and rendering the video inline in the chat interface via an interactive widget.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job in natural language and receive ranked GPU recommendations from a dozen cloud providers, based on estimated VRAM and FLOPs requirements. It surfaces the cheapest, fastest, and most balanced hardware options while disclosing the source and freshness of each price.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

A stateless remote MCP server that lets AI agents anonymously submit encrypted reports of alignment and safety failures to leaks.md (what others did) and confession.md (what the agent itself did), returning a receipt without accounts or keys. It exposes public tools such as submit_leak and submit_confession over Streamable HTTP.

Smart AI workflow for Cursor teams with intelligent prompt routing, real-time cost optimization, and team analytics.

Enables Codex to delegate code reviews, coding tasks, image/video generation, and background investigations to Grok via MCP tools.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive ranked GPU recommendations for cheapest, fastest, or balanced options across many cloud providers by estimating VRAM/FLOPs and aggregating live pricing.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe a fine-tuning workload and receive ranked GPU cloud recommendations based on required VRAM/FLOPs, cost, and runtime across multiple providers.

Enables users to describe a fine-tuning workload once and receive task-aware VRAM/FLOP estimates plus ranked cheapest, fastest, and balanced GPU options across multiple cloud providers. It can be used from Claude Code/Desktop or CLI/API to identify viable hardware options without manually comparing provider pricing.