"Setting up SQL Server connections for agent querying" matching MCP servers:

Enables LLM agents to run read-only SQL Server performance diagnostics through Query Store and DMVs, covering regressions, execution plans, parameter sniffing, missing-index impact, and wait statistics across databases.

Provides read-only SQL Server health diagnostics (server health, blocking queries, missing indexes) via MCP, with a GUI installer that automatically configures AI clients like Claude Desktop.

A Model Context Protocol server that enables Claude to query, monitor, and analyze SQL Server databases, with support for schema exploration, performance analysis, and optional write operations.

A Model Context Protocol (MCP) server for SQL Server / Azure SQL that enables querying, monitoring, and analyzing databases directly from Claude.

A production-ready MCP server for SQL Server monitoring, diagnostics, and database operations, with native connectivity, session management, deadlock and blocking analysis, and health checks.

A token-efficient MCP server for MySQL, PostgreSQL, and SQLite, providing tools to query databases, execute SQL, and analyze query performance.

Enables read-only querying and diagnostics for dual SQL Server 2019 instances with rate limiting and security controls, accessible via MCP tools over HTTP.

Enables AI assistants to interact with a Magento 2 or Adobe Commerce MySQL database through 50+ read-only tools for orders, catalog, customers, CMS, configuration, indexers, and diagnostics, with support for local DDEV, Commerce Cloud, and remote/SSH connections.

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 recording and querying MCP tool call traces, including latency, failures, and token costs, per agent run.

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

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

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.

Brings live project context into Slack via MCP, enabling developer teams to check service health, recall team decisions, search code, and query project context directly from chat.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

MCP-based production monitoring agent for manufacturing OEE, providing real-time monitoring, downtime root-cause analysis, and automated alerts through integration with SQL Server, MES APIs, and incident management systems.

Enables AI agents to inspect and manage Linux kernel-level eBPF security policies, including real-time status monitoring, policy retrieval, dynamic rule injection, and pre-execution SQL/syscall validation via natural language.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

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

Local-first observability for AI agent stacks. Provides MCP tools for agents to check health, shadow configs, and burnout metrics proactively.

Enables AI apps to inspect and manage a NextDNS account in plain language — profiles, analytics, query logs, blocklists, security and parental control settings — with every change saved and reversible via undo. It runs self-hosted on your own computer or server, keeps per-profile read/write/hidden permissions, and requires double confirmation for deletions or overwrites.

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.

Exposes network-monitoring tools (query metrics, analyze windows, compare, logs, status, runbooks, speed tests) as an MCP server for agentic workflows. Designed with evaluation suites, cost-aware model routing, and semantic tool retrieval.

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.

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

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.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Enables MCP clients to query and diagnose machine-learning training runs through read-only tools for listing experiments, reading configurations and final metrics, comparing runs, sampling metric curves, and detecting training failures.

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.