"React Query" matching MCP servers:

Enables read-only querying of Azure Log Analytics and Azure Resource Graph through MCP, supporting KQL queries, workspace discovery, and resource inventory exploration with Azure RBAC authentication.

Enables users to ask about render performance in React and React Native apps via natural language, exposing tools for listing render hotspots and explaining component re-renders.

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

Exposes a metrics query API to AI assistants, allowing them to issue queries and interact with the server's tools such as ping.

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

Allows developers and AI Agents to query Google Cloud Logging using natural language, translating queries into Google Cloud Logging Query Language (LQL) with Vertex AI Gemini 2.5.

Enables querying logs and metrics with plain SQL from any MCP client via pooml's read-only query API.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

Minimal MCP server that integrates with Graylog, enabling agents to search, analyze, and inspect log messages across streams, discover fields and streams, and query multiple Graylog instances.

Provides a local MCP interface to query captured, PII-scrubbed prompt telemetry from coding agents and browser chats, enabling time summaries, prompt search, and activity analysis without data leaving the machine.

MCP server that transforms raw log query results into structured incident reports (Markdown, JSON, HTML) with correlation to git commits and tickets.

MCP server for Prometheus metrics and observability. Give Claude (or any MCP-capable agent) read access to your Prometheus instance — query metrics with PromQL, inspect active alerts, and explore scrape targets without leaving the conversation. Tools: prometheus_list_metrics, prometheus_query, prometheus_query_range, prometheus_list_alerts, prometheus_list_targets.

Postgres performance MCP server that analyzes hotspots, explains query plans, and detects dead-weight indexes. It aims to produce evidence-backed recommendations through a verification loop.

MCP server for analyzing Perfetto traces with LLMs — query .pftrace files in PerfettoSQL via Claude Code or any MCP client

Every agent action is recorded in a SHA-256 hash chain. Prove to clients that your agent did what it said it did. Record, query, verify, and export agent activity.

Enables agents to query GPU availability across SSH hosts, find free GPUs, list hosts, and inspect fleet status using only nvidia-smi on the remote side.

MCP server wrapping the Akamai mPulse Query API v2 to fetch RUM aggregate data (summaries, histograms, per-minute time series) with loss-free numbers for downstream statistical analysis.

A drop-in Model Context Protocol (MCP) server that lets an AI agent query PostgreSQL — read-only, enforced at the database level, with real SQL validation, timeouts, cost limits, OAuth 2.1, and an audit trail.

Exposes network-monitoring tools (query metrics, analyze windows, compare, logs, status, runbooks, speed tests) as an MCP server for agentic workflows. Designed with evaluation suites, cost-aware model routing, and semantic tool retrieval.

An MCP server for Grafana Loki that enables LLMs to query, tail, and analyze logs, featuring fuzzy container-name matching, compact output, and tools for log patterns, volume, and label discovery. It can run as a local stdio server or a containerized HTTP service.