"Natural language interface for PostgreSQL database querying" matching MCP servers:

Enables comprehensive PostgreSQL database monitoring, analysis, and management through natural language queries. Provides performance insights, bloat analysis, vacuum monitoring, and intelligent maintenance recommendations across PostgreSQL versions 12-17.

Enables users to generate and execute InfluxDB queries using natural language commands in Korean. Supports querying metrics like CPU usage, memory status, and system monitoring data through conversational interface.

Full-featured MCP server that exposes 36 tools for interacting with PostgreSQL databases, covering schema introspection, query execution, data exploration, performance monitoring, security auditing, and maintenance.

MCP server for unified SQL database access, supporting multiple databases with schema discovery, caching, and safety controls.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

An unofficial MCP server that connects AI assistants to Akamai APIs, providing tools for edge diagnostics, debugging, and network analysis.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Shows your current Claude Desktop activity as Discord Rich Presence, including model, status, usage, and subscription info.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.

Bridges AI models with WinDbg to analyze Windows crash dumps and perform remote debugging through natural language queries, enabling execution of debugger commands and automated crash analysis.

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports.

Brings live project context into Slack via MCP, enabling developer teams to check service health, recall team decisions, search code, and query project context directly from chat.

Enables AI clients like Claude to triage, investigate, and operate Icinga installations through natural language, integrating with Icinga's REST APIs and providing deep awareness of monitoring plugins and historical performance data.

A remote MCP server that provides AI agents access to the Rootly API for incident management, allowing users to query and manage incidents, alerts, teams, services, and other incident management resources through natural language.

Unofficial MCP server for Vexo analytics. Enables querying event names, counts, timelines, sessions, and recent events from Vexo Export API through natural language.

An MCP server that enables natural language querying and troubleshooting of Kubernetes clusters, supporting multi-cluster switching and providing tools for pods, nodes, events, and logs.

Provides global internet resource intelligence by querying RIRs for IP and ASN data, routing visibility, and network health. It enables users to perform RPKI validation, BGP inspection, and historical allocation analysis through natural language or a REST API.

Read-only PostgreSQL performance auditing via MCP. Collects metrics from PostgreSQL system views, applies deterministic checks for index, query, vacuum, configuration, and connection issues, and returns structured findings for MCP clients.

MCP server that enables querying an OpenObserve instance for logs, traces, and metrics via tools like search_logs, search_traces, get_trace, and query_metrics, supporting natural-language-driven observability workflows.