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

MCP server that integrates sql-sop SQL linter into LLM clients, enabling linting SQL queries and listing lint rules via chat tools.

Runs SQL statements in memory against real PostgreSQL 18 and SQLite 3.49 engines after loading your schema and sample rows, returning each statement's own verdict: errors with line, column, and hints, constraint violations, or the resulting rows. It also compares both dialects to expose portability differences, all with no database server, network, or API key.

MCP server for operating and testing Android devices with AI agents. Provides observation and action tools for interactive control and spreadsheet-driven test runs.

Agent Ready is an AI agent readability scanner — point it at any public URL and get back a 0–100 score plus per-check remediation hints for every failing check. This package wraps the same engine that powers agent-ready.dev as an MCP server, so Claude Desktop, Claude Code, Cursor, Cline, VS Code, and Windsurf can run scans inline and explain the results.

MCP server exposing statistical regression testing for LLM agents as a "run" tool: p-value, effect size, and confidence interval on whether agent behavior actually changed.

Scans any website to generate an Agent Readiness Report based on the ASO framework, evaluating agent discoverability, trust, interoperability, and commerce readiness.

Evaluates MCP servers by running read-only checks and returning a graded report with an A-F letter grade.

MCP server that gives an AI agent a throwaway test identity — a real disposable email address and a real UK phone number — so it can sign up for something it's testing and read back the verification email/SMS itself, without a human in the loop.

Enables coding agents to build, drive, and debug Android apps on a real device, with screenshots, UI dumps, local vision model analysis, and lock-safe Gradle builds.

Local static inspection of MCP and AI-agent tool manifests before attachment, reporting findings and missing evidence without executing proposed tools. Results do not establish runtime safety.

MCP server that scores tool descriptions, estimates token costs, simulates agent tool selection, and generates reliability reports to help AI agents choose the right tools and reduce wasted tokens.

Enables AI coding agents to see, measure, and verify web pages through a real Chrome browser, including screenshots, responsive layout and accessibility audits, pixel diffing against baselines, secure logins, and deployed-fix verification.

An MCP server that lets an AI agent drive the iOS Simulator in a loop, enabling tapping, typing, swiping, reading the screen via the accessibility tree, and verifying app state.

Provides tools for managing tasks, searching docs, and running a heuristic quality gate to check agent output for common issues before human review.

A deterministic behavior-compatibility layer for AI agents that checks normalized event traces against operating contracts and compares baselines with candidates to catch regressions in approval, stop, scope, recovery, and completion rules.

Enables an AI agent to boot and control an Android emulator — tapping, typing, scrolling, inspecting screens, reading logs and installing APKs — and to record those interactions as replayable, CI-ready test flows. Actions are captured by element text or id rather than coordinates, so the saved tests survive layout changes and rerun without the agent.

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

Enables style verification and CSS inspection through Chrome DevTools Protocol, using JSON specs to check computed styles, find elements, and interact with pages.

Discovery router for the agent-device CLI with status, install, help, prompts, and resources.

Local MCP server for observing and controlling an authorized Android device over USB using ADB and scrcpy, providing screen capture, UI automation, app inspection, logcat, and evidence recording.

A local MCP server that controls Android devices via ADB, enabling AI agents and users to manage devices, execute UI automation, and perform testing through 27 MCP tools and an optional web console.

A neutral verification court for AI tools that ranks MCP servers by executing them against ground truth and recording results. Enables agents to consult execution records, contribute verdicts, and challenge claims.

Builds, validates, and render-tests Panzoid Shader Object JSON in a real WebGL 1 Chromium context, exposing these capabilities as MCP tools.

Enables recording of live LLM HTTP/SSE token streams and MCP stdio interactions into deterministic cassettes, then replaying them offline in CI in ~0ms with no network calls or API keys. Normalizes volatile headers, hashes prompts and tool calls, and scrubs secrets while enforcing SSRF and path-containment safeguards.

Exposes three read-only tools that list local Agent and MCP runtime candidates, inspect running processes, and check a live runtime against an approved artifact expectation. Returns explicit verdicts such as matched, stale, leaked, conflict, unknown, or not running without exposing argv, environment values, or file contents.

Enables external agents to rehearse calendar scheduling tasks against simulated tools with injectable failures, inspect tool traces and resulting state, and verify outcomes before acting in reality.

Lets an AI coding agent journal its edits, commands, checkpoints, intents and test evidence locally, then compile them into a machine-readable Change Package plus a human-readable review summary with statically computed risks. Optionally publishes the result as a GitHub Draft PR with inline comments on the exact diff lines, always gated behind explicit confirmation and redaction.

Enables coding agents to start and operate live kernels on local machines, Modal GPUs, or Colab VMs, running code, notebook cells, shell commands, and file transfers while inspecting variables, plots, and errors. It also runs notebooks, scripts, and web UIs end to end as budget-capped plans that verify expectations and report what failed and what changed.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

An MCP server that lets an AI agent drive and inspect a running Next.js app, with headless route/error introspection and a real DOM-based in-page bridge for interactive testing.