"A server for querying Oracle SQL database schemas" matching MCP servers:

Enables read-only querying, evaluation, challenge, and judging of an Oracle project's rules and evidence without modifying the filesystem, plus task tracking for agents.

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 enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables AI coding agents to verify code patches locally with sub-50ms neuro-symbolic analysis, detecting structural issues like dependency cycles and interface drift without consuming output tokens.

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

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

An MCP server that analyzes SQL schemas, generates realistic test data, benchmarks queries, and provides optimization recommendations to improve database performance.

Enables AI assistants to perform QC Database tasks such as uploading records, managing project turnover packages, and signing off on inspections via natural language.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Zero-sidecar MCP server embedded directly in the Godot 4 editor as a GDScript EditorPlugin — no Node or Python sidecar. Reflection-driven tools over any engine class, validate-before-write GDScript, scene/script/resource authoring, and a runtime play-test loop (play, screenshot, input, assert).

A simple server implementing the Model Context Protocol (MCP) that echoes messages back, designed for testing MCP clients.

Bridges AI models with WinDbg to analyze Windows crash dumps and perform remote debugging through natural language queries, enabling execution of debugger commands and automated crash analysis.

Enables LLMs to perform browser automation tasks including web scraping, taking screenshots, and executing JavaScript using Playwright. It facilitates real-time interaction with web pages and the generation of automated test code.

MCP server that brings Trustabl static reliability scanning into Cursor, letting agents inventory agents/tools, flag risky patterns, score production readiness, and write SARIF reports.

Evaluates AI agent and MCP server competency across 6 quality dimensions using challenge-response tests, returning quality scores and W3C Verifiable Credentials.

Translation quality evaluation MCP Server powered by xCOMET. Provides quality scoring (0-1), error detection with severity levels, and optimized batch processing for AI-assisted translation workflows.

Official MCP server for Qase Test Management Platform. Enables AI assistants to manage test cases, test runs, defects, and other Qase entities through natural language.

MCP server that exposes the JsonFabrica API as tools, so an AI agent can generate realistic synthetic JSON test data while it codes — single documents or full relational batches from reusable templates, reproducible with a seed. Runs locally over stdio; no network service.

Connects AI agents to Minecraft servers via RCON to execute commands, monitor logs, and perform read-only SQLite database queries. It is specifically designed to facilitate AI-assisted plugin development, live debugging, and automated testing workflows.

Exposes Semgrep as deterministic, rule-matching analysis tools over MCP so a code-review agent can scan only the lines changed in a diff, scan whole files on demand, and check analyzer availability, version, ruleset, and limits. Results are returned as compact per-finding lines with source snippets suitable as comment anchors, along with counts of suppressed out-of-diff matches.