"A server for finding information about GitLab, the DevOps platform" matching MCP servers:

Provides DevOps and testing tools for full-stack applications, including UI testing, security audits, CI/CD validation, and Rust/Tauri support.

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

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

A local MCP server that connects Claude Desktop, Claude Code, and Claude Chat to your Azure DevOps organization, enabling querying work items, reviewing pull requests, triggering pipelines, searching code, managing test plans, and updating wikis using plain language.

Enables Claude Code to read and write issues on the Testing Platform via natural language, including listing projects, filtering issues, creating and updating issues, and adding comments.

Enables test execution and management through MCP clients, allowing retrieval of projects, listing tests, executing tests with browser selection, and monitoring results.

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.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Enables AI agents to interact with 1C: Enterprise development environment, including running tests, managing launch profiles, building configurations, and performing database operations through the 1C: Platform Tools extension.

Enables a language model to run four deterministic data checks it cannot argue with — column-contract validation with an explicit quarantine list, arithmetic reconciliation of opening/movements/closing, distinct entity counting against row counts, and duplicate detection returned for human review — each answering with structured findings and row/column locators. Failures come back as results with reasons, so the model reports exactly which rows and columns are broken rather than asserting the data is clean.

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.

Enables AI assistants to interact with Azure DevOps projects, including work items, test results, Git repositories, and dependency analysis via the Model Context Protocol.

An LLM-driven API testing platform exposed over MCP, enabling declarative test definition and DAG-based execution.

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 coding agents to audit public or localhost pages, gather structured SEO evidence, and retrieve compact reports, finding details, and next actions for triage.

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.

Enables AI agents to create webhook URLs, wait for incoming deliveries, inspect payloads, and replay or send signed webhook events to a local handler.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.