"A Python server using FastAPI to expose public web API endpoints to an agent" matching MCP servers:

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.

Enables Claude Desktop to access the hosted Web Speed API through MCP, requiring only an API key for setup.

Enables external AI clients to autonomously execute backend tools via SSE, including system health checks, within a FastAPI async microservice with conversational memory.

A production-ready Model Context Protocol server built with FastAPI, featuring JWT authentication, PostgreSQL database support, Redis caching, and comprehensive health monitoring for building secure async API applications.

Enables live website health checks including TLS, HTTPS, and security headers, returning an A-F grade with specific fixes.

MCP server that exposes 31 OSINT checks from Lissy93/web-check as tools for website analysis, including SSL, DNS, headers, WHOIS, and security presets. Enables natural-language-driven web recon and health checks.

Enables web performance analysis using Google PageSpeed Insights and Chrome UX Report APIs, providing compact metrics and diagnostics without local browser automation.

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

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.

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

Registers a set of inert, tempting decoy tools with any MCP-capable client so that any call from a hijacked or prompt-injected agent instantly raises an alert with full attack context and a per-call trace token. Also plants worthless canary tokens in honeypot files that trigger zero-false-positive leak alerts when they surface in agent output, files, or outbound requests.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

A read-only MCP server for querying AI provider administration APIs, providing normalized usage, cost, and dashboard data for OpenAI and Anthropic.

Unified MCP safety server that detects prompt injection (75 patterns), scans LLM outputs for leaked secrets/PII, enforces API cost budgets, and creates signed audit trails. Zero ML dependencies, pure Python.

Enables agents to report and query performance metrics to build a community-driven quality database for MCP tools. This server helps agents discover and select the most reliable tools based on success rates and user-reported quality scores.

Enables agents to monitor websites for content changes with persistent baselines, durable notifications, and explicit review acknowledgement, using direct HTTPS fetches or isolated Playwright browser rendering for dynamic pages.

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

MCP server that provides structured audit logging for AI agent repair tasks via tools to start, record, end, query, and export event traces, with JSONL persistence and SDK integration.

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