"An example MCP for client prototyping in a business context with a public API call" matching MCP servers:

A demonstration MCP server that fetches web content and monitors system information via SSE.

Enables client success teams to get instant, natural-language health reads on client accounts with tools for overall health, low-performing courses, and launcher-only clients.

A hosted Model Context Protocol server that lets AI agents triage review queues, investigate authors, tune policies and wordlists, respond to incidents, configure projects and webhooks, and report on moderation activity through a Moderation API account.

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.

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

Enables tracing mcp-use tool callbacks and capturing tool errors and exceptions in Sentry via MCP middleware, without SDK changes or additional services.

Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.

Enables auditing MCP servers' tools by their estimated context token cost, ranking verbose names, descriptions, and schemas before they consume the agent window. Can run as an MCP tool so agents can budget their own context.

Dependency health checker for AI agent skills. Analyzes external endpoints for uptime, SSL validity, domain reputation, ownership changes, and abuse scores. Returns a 0-100 trust score per endpoint.

Measure the network path quality to a voice/VoIP destination and get an estimated MOS (ITU-T G.107 E-model) plus live RTT, jitter, and packet loss. Agent-native: REST + MCP, free tier then pay-per-call via x402.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

Enables monitoring chosen API vendors and public URLs, comparing each source with its saved baseline and returning only new changelog entries, status incidents, OpenAPI operation changes, and Sunset or Deprecation header updates.

Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.

Enables AI assistants to monitor and control Bambu Lab 3D printers over a local network via MQTT, including real-time status, AMS filament info, print control, and maintenance tracking. It supports guided setup, safety confirmations, read-only mode, and a built-in printer simulator.

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

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

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

Monitors API endpoint health by checking availability, measuring response times, and providing detailed status reports with error handling and timeout protection for the localhost:8080/api/ticket endpoint.

Read-only MCP server that exposes governed operational records with provenance and validity windows so clients can check which evidence is still current before answering. It lets agents reconcile freshness, schedule state, and evidence specificity to determine whether scheduled automations are actually healthy, rather than trusting an expired dashboard summary.

Manage and monitor Hadoop clusters via Apache Ambari API, enabling service operations, configuration changes, status checks, and request tracking through a unified MCP interface for simplified administration. * Guide: https://call518.medium.com/llm-based-ambari-control-via-mcp-8668a2b5ffb9

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.

Bridges with an installed API monitoring application to enable launching and status checks, live background capture, process attach, filter-tree inspection, GUI actions, and opening, validating, decoding, searching, inspecting, exporting, and comparing saved captures and related metadata.

Enables AI agents to access real-time Windows PC context including active window, system performance, screen time, productivity analytics, and historical usage through MCP tools.

Enables AI assistants to explore a PostgreSQL database, run bounded read-only SQL, inspect query plans, test hypothetical indexes, find slow queries, and check database health with compact CSV output.

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

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

This MCP server enables secure, read-only access to synthetic preventive-care patient records and bounded IRIS namespace health metrics through a small set of allowlisted, parameterized tools.

Per-customer API usage analytics and billing exports for small B2B APIs. Agents can sign up, get ingest snippets and read usage by customer, endpoint and plan.

An MCP server that provides access to Google Cloud Monitoring API, enabling interaction with cloud resources monitoring data through natural language commands.