"MCP servers built with languages other than Python and TypeScript" matching MCP servers:

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

A Model Context Protocol (MCP) server for comprehensive monitoring and observability of systems using Langfuse.

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.

Enables natural language queries for device support, including resolving device references, retrieving device details, historical metrics, and checking provisioning status. Maintains conversation context for multi-turn interactions.

A stateless MCP server for incident lab operations, exposing MCP and health endpoints, with conformance testing.

Self-hosted Nightingale (n9e) includes a native Streamable HTTP MCP server at /mcp for querying alerts, rules, metrics, logs, hosts and dashboards with the caller's existing permissions. Users deploy the Nightingale server themselves; the implementation and deployment documentation are in the main repository, and the official website is https://n9e.io.

Enables real-time data streaming through Server-Sent Events with timestamp broadcasting and server monitoring capabilities. Optimized for deployment on Render.com with health checks and status endpoints.

Enables MCP clients to retrieve lab performance scores and field Core Web Vitals data for web pages via Google APIs.

Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.

Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.

Enables real-time system monitoring and automation through MCP protocol with SSE transport, integrating with n8n workflows to check system health, query logs, and retrieve metrics from ABC system APIs. Supports natural language queries in Vietnamese and English for seamless system administration.

Enables agents to fetch app reviews from Apple's App Store and Google Play per country store, newest first, filtered by date or star rating, with each scheduled run returning only reviews not seen before. It also returns a per-app, per-country rating trend row with the current rating, rating count, change since the last run, and the star split of new reviews.

Checks MCP servers by URL, npm package, or official MCP Registry search, reporting availability, response speed, exposed tools, resources, and prompts. It flags changes since prior runs as breaking, warning, or info using saved snapshots.

Lets your coding agent check whether a production deploy actually worked. It waits for DeployAngel's verdict on a release (cleared, failed, or not cleared yet) and reads the evidence when one fails, including jobs that quietly stopped running.

Enables analysis of AI agent traces to detect and fix failures using heuristic detectors, with no LLM calls required.

Enables AI assistants to programmatically create, execute, and analyze Apache JMeter performance tests. It supports automated bottleneck detection, report generation, and distributed testing management through natural language.

Provides tools for obtaining system CPU and memory statistics through FastAPI endpoints exposed via MCP.

Enables local computer diagnostics by sampling CPU, memory, disk, and network activity and displaying the busiest processes in an interactive dashboard. It also supports human-confirmed process-stop proposals while keeping diagnostics local and bounded.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.

Enables interaction with Convoy's webhooks proxy API for managing and monitoring webhook delivery, events, and configurations through natural language.

Enables AI agents and users to manage workspace files, monitor system metrics, take persistent notes, and retrieve weather data via MCP tools and resources.

An MCP server implementation for accessing Google Analytics 4 (GA4) data, built using the Model Context Protocol TypeScript SDK.

A clean, simple Model Context Protocol (MCP) server that integrates Firebase Crashlytics with BigQuery for AI-powered crash analysis. Built for use with Claude Code and other MCP-compatible AI assistants.

Agentic tool that looks for statistical variations in conversation structure and logs unusual events to a SQLite database. Built using the Model Context Protocol (MCP), this system is designed to be used with Claude Desktop or other MCP-compatible clients.

Enables AI agents to search service logs, group errors by signature, compare a candidate run against a baseline for regressions, and correlate which problems from other services appear before or after an error. All results are computed by the tools rather than the language model, so agents can only explain findings the code produced.

Enables real-time sliding-window token velocity anomaly detection and adaptive rate-limiting through a native STDIO MCP server built entirely on the Python standard library. Exposes deterministic algorithmic checks that report anomalies and throttle behavior as JSON tool results for MCP clients such as Claude Desktop, Cursor, and Antigravity.

A powerful, lightweight Model Context Protocol (MCP) server built in Python that enables Artificial Intelligence assistants (Claude Desktop, Cursor, Antigravity, Claude Code, etc.) to manage and monitor your Proxmox VE cluster using natural language.

Enables natural-language querying and analysis of AWS spending and model-invocation usage, including detailed breakdowns by service, region, account, day, and instance type.

Enables users to query and analyze cloud spending, EC2 cost breakdowns, service-level spend, and model usage logs through natural-language interactions. It also supports cross-account spend retrieval when configured.