"Automating HTTP API Wrapping into MCPT Tool" matching MCP servers:

Provides a unified MCP server to interact with multiple middleware (Loki, MySQL, Redis, RocketMQ, Elasticsearch, MongoDB) through a desktop application for connection management and configuration export.

Enables live tracing and observability of MCP tool calls, persisting execution traces to MongoDB and streaming them in real time to a dashboard.

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.

Enables endpoint availability monitoring and HTTP health checks for single or multiple URLs.

Monitor and manage Apache Airflow clusters through natural language queries via MCP tools: DAG inspection, task monitoring, health checks, and cluster analytics without API complexity. * Guide: https://call518.medium.com/mcp-airflow-api-a-model-context-protocol-mcp-server-for-apache-airflow-5dfdfb2

An enterprise MCP server scaffold that provides secure, governed access to internal tools through RBAC, audit logging, rate limiting, and prompt-injection boundaries, with a FastAPI control plane and OpenTelemetry observability.

Provides MCP tools to perform Looker health checks (health_pulse, health_analyze, health_vacuum) via the MCP Toolbox connector.

An MCP server that enables interaction with Google Cloud Logging API, allowing users to write, read, and manage log entries and configurations through natural language.

A unified local API gateway providing caching, rate limiting, and full Model Context Protocol compatibility for AI agent integration. It enables users to aggregate multiple API endpoints into a single gateway with built-in observability and customizable eviction strategies.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

Enables AI agents to diagnose Linux server incidents by collecting and structuring system diagnostics from multiple servers via SSH, with tools for finding incident clusters, gathering context (memory, CPU, swap, etc.), and running arbitrary commands.

stateless MCP server — fresh instance per request, infinite horizontal scaling

Connects AI assistants to live VuNet observability data, allowing querying metrics, browsing data models, and checking connection status.

Provides system hardware information (CPU, memory, storage, etc.) and system status (OS, network, resource usage) via MCP tools.

A comprehensive Linux system performance profiler with MCP remote invocation support, featuring advanced process profiling and flame graph generation.

Monitor the real-time status of 200+ popular APIs and services. Check if services like GitHub, Stripe, AWS, and Slack are experiencing outages or degraded performance directly from your AI assistant.

An enterprise AI control plane with an MCP gateway for connecting agents to tools, enforcing identity-based access and security policies, and recording activity.

Provides secure access to the LogicMonitor API, enabling AI assistants to manage monitoring infrastructure through natural language commands.

Enables LLM clients to manage Uptime Kuma HTTP(S) monitors, groups, tags, and notifications, and to retrieve heartbeats, stats, and incident history through MCP tools.

Enables AI agents to explore and, when writes are enabled, modify a Langfuse deployment — traces, observations, sessions, prompts, datasets, scores, metrics and more — through the full Langfuse public API. Supports Langfuse Cloud in any region and self-hosted instances, including corporate networks that re-sign TLS traffic with their own certificate authority.

A paid, hosted MCP server that acts as a witness layer for AI agent tool calls, providing receipt creation, approval evidence, argument redaction, result summaries, and audit exports.

Uptime monitoring from your AI agent: create and diagnose checks (40 types: HTTP, TCP, DNS, SSL, ICMP, databases, message queues and more), read results and incidents, and manage status pages and maintenance windows. Built into the self-hostable SolidPing server, served over streamable HTTP at /api/v1/mcp.

MCP server wrapping the Akamai mPulse Query API v2 to fetch RUM aggregate data (summaries, histograms, per-minute time series) with loss-free numbers for downstream statistical analysis.

Enables AI assistants to search Elasticsearch logs, retrieve log details, analyze service health, scan local codebases for APIs, and create Kibana dashboards.

Enables checking whether MCP servers are alive, maintained, and safe to use by scoring metadata health and trust, scanning MCP config files, and optionally wrapping a server to log tool calls and errors locally.

MCP production gateway — Streamable HTTP proxy to stdio/HTTP backends with API-key auth, per-key rate limiting, TTL cache, per-backend circuit breaker, Prometheus metrics.

A flight recorder for Claude Code that captures tool calls and observations into a SQLite database, then classifies them into a distilled timeline for incident troubleshooting.

Enables MCP clients to call a hello tool and access observability features, including metrics and request traces, through a Streamable HTTP transport with SQLite-backed storage.

Enables Windows event log forensics by wrapping Hayabusa, offering EVTX scanning, Sigma rule exploration, ATT&CK coverage analysis, and detection engineering resources.

Enables AI assistants to run Sumo Logic log searches via HTTP using the Search Job API.

Enables per-profile tool filtering and enforcement for one or more upstream MCP servers, exposing only allowed tools and rejecting blocked calls, with Streamable HTTP serving and observability.

Enables Windows Event Log (EVTX) analysis by wrapping Hayabusa, exposing scan and rule retrieval tools.

Enables AI clients to connect through a single multiplexed SSE/HTTP endpoint to multiple local and remote MCP servers, with token authentication, per-tool approval policies, tool namespacing, and live telemetry.

Enables tracing every tool call from a ChatGPT app into LangSmith, one run per tool call, grouped per conversation thread. Provides thread-level evaluation and monitoring of tool interactions.

MCPGate aggregates multiple MCP servers into a single unified endpoint, enabling centralized tool management with granular filtering, automatic namespacing, and observability. Features a real-time web dashboard and optional PostgreSQL-backed audit trails for monitoring and controlling AI tool access across local and remote deployments.

Enables secure multi-tenant HTTP/SSE access to MCP tools with bearer-key authentication, per-tool rate limiting, sensitive-data redaction, and structured audit logging through an ASGI-compatible gateway.

Captures AI agent runs and turns them into tamper-evident execution records showing tool use, timing, failures, recoveries, and human interventions. Records can be inspected, exported, and verified offline.

Enables AI assistants to interact with DataDog's observability platform through a standardized interface. Supports monitoring infrastructure, managing events, analyzing logs and metrics, and automating operations like alerts and downtimes.

Publishes an MCP Streamable HTTP JSON-RPC endpoint where clients can initialize sessions, list and call synthetic deployment, runtime, cluster-inspection, and audit-log tools. While serving those decoy tools, it records initialization, tool discovery, argument shapes, and multi-tool sequences as evidence for classifying and investigating agent-like behavior.

Exposes a curated subset of the CentralOps HTTP API as typed tools for Claude Code, enabling drift triage, mapping edits, backfill jobs, and quarantine reprocess without raw HTTP calls.