"AT&T" matching MCP servers:

Exposes homelab and IT-ops tools to Claude, including system health monitoring, Grafana alert states, Docker container status, Loki logs, SMART disk health, and more.

Prediction stability engine for AI agents. Evaluate model stability, detect ghosts, probe any LLM for instability, monitor fleet drift. 20 tools + 1 resource. Works with Claude, Cursor, VS Code.

An MCP server for controlled server operations via BaoTa Panel, providing 50+ tools for system, site, database, files, cron, Docker, and network management with safety features like permission tiers, credential masking, and audit logs.

This MCP server enables AI assistants to interact with a BT Panel server through natural language, allowing users to query logs, manage websites, and monitor system status without manual panel login.

Enables AI clients to create and configure monitors, subscribe to their evidence streams with durable cursors, and process observations with guaranteed at-least-once delivery.

Enables AI agents to read Grafana dashboards, alert rules, and query datasources (Prometheus, Loki, etc.) through MCP tools, integrating Grafana monitoring into the IDE.

Enables AI clients to perform safe, read-only IT diagnostics and retrieve local runbooks, asset records, and knowledge articles through MCP, with allowlisted network checks and audit logging.

Enables read-only querying and analysis of Kibana logs through a logged-in sid Cookie, including keyword/time/index searches, traceId lookups, surrounding log context, and aggregations by service, level, or host. It also validates Kibana connection status and redacts sensitive fields such as cookies, tokens, and passwords.

Enables external CLI agents to directly operate TauriTavern via MCP, including reading runtime data, executing STScript, sending messages, switching characters, and running JavaScript diagnostics in iframes.

Exposes Kubernetes cluster management tools to LLMs, enabling querying pods, deployments, logs, metrics, and managing port forwards via natural language.

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

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Enables natural language queries of the Chrome UX Report API to fetch Core Web Vitals (LCP, INP, CLS, FCP, TTFB), compare form factors, benchmark pages against site averages, and analyze up to 40 weeks of performance trends.

Enables an LLM agent to investigate a Kong Gateway through fourteen read-only, schema-validated tools and a deterministic diagnostic engine, so it can trace routes, services, plugins and upstream health and explain faults such as why authentication is failing on a path. Every fact comes from the gateway itself, and the model only selects tools and writes the evidence-backed explanation.

Model Context Protocol server for Verlon AI that exposes gates, logs, recommendations, and experiments as MCP tools, enabling coding agents to inspect and manage AI infrastructure natively.

Enables MCP-speaking agents to execute tool calls through an authorization gate, producing signed decision records and reconciled effect rows for accountability.

MCP server for Healthchecks: inspect, create and adjust cron and uptime checks, and read why one failed

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.

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.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.