"How to obtain an API key" matching MCP servers:

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

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.

Self-hostable uptime monitoring with an MCP server for managing monitors, incidents, and status pages. Runs with Docker and Postgres, with Streamable HTTP, OAuth, and API key authentication.

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

Enables querying PostHog with HogQL, retrieving event and property definitions, session replays, and project lists, plus optional REST API access with safeguards against accidental writes and API key leakage.

FastMCP server with read-only tools to monitor Redis — queue depths, Celery queue status, connected clients, server/memory info, and per-database key counts

Enables AI agents to obtain deterministic, structured JVM production diagnostics—including deadlocks, hot locks, bottleneck frames, and planned GC/heap analysis—from thread dumps, GC logs, and heap histograms without hallucinating from raw logs.

Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.

A MCP server for tracking AI usage metrics and structured logs across applications. Monitor model calls, analyze usage patterns, track costs, and debug AI interactions.

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.

MCP server for measuring, tracking, scoring, and improving AI agent reliability with tools for recording interactions, scoring reliability, analyzing failures, recommending improvements, generating audit reports, and checking MCP health.

Enables LLM clients to access DevSecOps tooling such as CI/CD pipeline status, vulnerability triage, log search, and dependency scanning through MCP, turning AI copilots into security-aware engineering partners.

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.

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.

Enables MCP clients to query an organization's Traceorb telemetry data through the same GETs the panel uses, using a read key for authentication.

Connects AI assistants to the Vectra AI security platform to enable intelligent analysis of threat detection data and automated incident response workflows. It allows users to investigate threats, take response actions, and generate security reports using natural language.

Perimeter security for your AI Infrastructure.

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

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables AI assistants to monitor and analyze industrial process data from DCS/SCADA systems via MCP protocol, with domain expert knowledge for anomaly detection and recommendations.

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.

Connects AI-powered tools to the Confident AI platform for evaluating, observing, and iterating on AI quality, enabling cloud evaluations, dataset management, prompt versioning, production tracing, and human annotations directly from editors like Cursor and Claude Code.

Enables MCP clients to enforce local no-harm tool policies, require single-use operator approval for sensitive actions, and produce verifiable audit records.

Ships only the MCP server, not the recorder. Recording works by wrapping a process on the host — a local proxy, a PATH shim, a shadow git index. None of that survives a container boundary, so orca record inside here would watch an empty namespace. What does travel is the read side: the six MCP tools serve runs that already exist on disk, so a client mounts its .orca directory and r

A read-only MCP server for the Auralogs logging platform, enabling AI agents to query production logs, search errors, and retrieve AI analyses without write access. It allows you to ask your coding assistant 'what's broken in production right now?' by connecting to your logs via a simple bearer auth key.

Enables users to log AI tool activity without conversation text and contribute back only user-approved, small-cell-suppressed counts through a local MCP interface.

Enables AI incident-response agents to connect over MCP to a simulated service environment, receive alerts, gather diagnostic evidence, execute safe SOP actions, and verify recovery.

Enables comprehensive integration with Azure AI services including OpenAI, Cognitive Services, Computer Vision, and Face API through a mission-critical MCP server. Provides enterprise-grade reliability with high availability, observability, chaos engineering, and secure multi-region deployment capabilities.

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

An MCP server for the Liongard platform, providing tools to manage inspectors, systems, metrics, alerts, and environments through Liongard's API.

Enables read-only reporting of Anthropic organization usage and costs through a user-connected admin API key, across any MCP-compatible client.

Transforms Elasticsearch into an AI-powered observability engine for analyzing logs, APM traces, and system metrics through natural language.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

AI Ops - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables LLMs to query real job data and summarize backend job failures using MCP tools backed by a SQLite database.

Enables MCP clients to read Datadog account data, including metric timeseries and metadata, dashboards, log search results, incidents, monitors, and downtimes, using an API key and application key.