"How to Find and Fix Bugs in a React Native App" matching MCP servers:

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.

A production-grade MCP server for FIX protocol trading operations that enables order management, session repair, and algorithmic execution. It provides specialized tools for monitoring session health, managing ticker reference data, and executing complex trading scenarios.

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.

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.

Grammar Fix AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables validation of FIX messages against onboarding rules and automates onboarding workflows through MCP tools, using a LangGraph agent with LLM-based parsing and error explanation.

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.

AI-powered MCP server for file renaming, organization, and PDF splitting using natural language prompts and content analysis.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

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.

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.

Enables applications to scan model replies for leaks, sanitize output, authorize model-proposed actions with signed single-use grants, and optionally score prompt injection in a way that can only tighten enforcement decisions.

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

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

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 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 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 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 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 get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

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.

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.