"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.

MCP server for safely exposing SQL Server database capabilities to LLM clients, with read-only mode, security features, and observability.

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.

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.

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

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

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 developers to build production-grade MCP services with dependency injection, zero global state, layered imports, and a sample user-notes tool, resource, and prompt. It includes Testcontainers, Prometheus metrics, Kubernetes probes, and structured logging for testable local and production deployments.

Enables an AI assistant to diagnose a broken application on a local Kubernetes cluster by inspecting pods, events, and logs, with server-side guardrails, least-privilege RBAC, and an audit log. It can also restart deployments in allowed namespaces only after a dry-run preview and host approval.

Enables remote log searching, tailing, and file reading across servers via MCP tools, with pluggable execution via SSH, pyinfra, or local commands.

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.

Enables natural language analysis of network packet captures, including protocol detection, flow analysis, and security threat identification, integrated with AI assistants.

Provides governed MCP tools for querying optionally restricted Microsoft Sentinel/Log Analytics tables and optional Defender XDR hunting data, retrieving incidents and entities, and—for agent identities only—adding attributed incident comments, with Entra ID authentication, KQL validation, rate limits, and redacted audit logging.

Record Python/Rust/TypeScript program runs to local SQLite; query call trees, exceptions, and value flow over MCP — refuses rather than guesses. Not the unrelated Telegram sensorium-mcp.

MCP server for Python JSON logs

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

Monitors development server logs in real-time and provides Claude with immediate error notifications via Server-Sent Events. Intelligently parses TypeScript, Svelte, and Vite errors with severity classification and file correlation.

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 an LLM to triage packet captures with nine tools that return capped summaries of conversations, talkers, protocols, DNS, HTTP, and TLS, then drill into individual packets, with every finding citing the packet number that proves it. Reads classic pcap and pcapng files using only the Python standard library, so no tshark or other external binary is required.

A TypeScript MCP server for querying Shopee logs via the log platform's Mgmt API and Querymaster WebSocket flow, with config-driven service resolution and session reuse.

A local-first MCP server that gives AI coding agents runtime visibility and AI-managed debug logging. It replaces blind print() debugging by turning runtime execution into causal chains, allowing agents to instantly locate bugs by finding missing .success events in Python and TypeScript code. Single binary with MCP, CLI, and HTTP interfaces.

An MCP server for querying Grafana Loki directly with a discovery-first workflow — labels, values, series, and LogQL queries without requiring Grafana.

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

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

Provides cryptographically signed, tamper-evident compute usage receipts via a Model Context Protocol server, exposing tools to record usage, verify receipts, list the ledger, and verify ledger integrity without requiring any account, API key, or single cloud provider.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

Web analytics and AI session replay for agents: visitors, breakdowns, funnels, goals, revenue attribution, and AI-found site issues. Connects to the hosted Flowsery MCP server.

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