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

MCP server for AI-assisted trading operations, enabling agents to diagnose and resolve FIX, OMS, and venue incidents through controlled tools and human approval.

Streams live React profiler data to AI agents, enabling them to analyze performance and suggest specific, actionable fixes for slow components.

Enables AI assistants to inspect React Native app performance including memory, rendering, frames, and network requests, providing plain-language summaries and shareable reports.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

MCP server for Uptrack uptime monitoring. Manage monitors and incidents from AI agents like Claude, ChatGPT, and Cursor.

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

A lean MCP server that provides LLM agents with transparent access to multiple Prometheus instances for metrics analysis and SRE operations.

A production-oriented reliability, benchmarking, and evaluation system that layers multi-version protocol negotiation, adaptive streaming, connection pooling, and resilience onto an MCP agent runtime.

BeamScope gives AI coding agents access to your running BEAM application through a resilient TCP architecture that survives app restarts.

iOS leak hunting and performance investigation. Reads .memgraph and .trace files, classifies retain cycles against a 34-pattern catalog with Swift fix templates, bridges to source via SourceKit-LSP. 28 MCP tools, 34 catalog resources, 5 investigation prompts.

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

A specialized diagnostic framework that enables AI models to self-diagnose and fix MCP-related issues by analyzing logs, validating configurations, testing connections, and implementing solutions.

Seamlessly bring real-time production context—logs, metrics, and traces—into your local environment to auto-fix code faster.

Runs, tests, and finds issues in your Python services with zero code changes, then helps your AI agent fix what breaks and proves it with acceptance tests.

Retrieves captured bug context and evidence for coding agents, enabling progressive disclosure of sessions, manifests, and window correlations to diagnose and fix issues.

MCP-native health monitoring that probes MCP servers using the list_tools protocol handshake, detects version drift, stores history in SQLite, and generates an HTML dashboard.