"stack and dns mcp" matching MCP servers:

Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp

Website audits for AI agents: cookie consent and tracking, Core Web Vitals and lost revenue, WCAG 2.2, email/DNS security (SPF, DMARC) and AI-search readiness, merged into one prioritised fix plan.

Drives a browser - or a native Expo/React Native app - and your dev server, putting both on one correlated timeline so a console error and the backend stack trace from the same moment line up. For Claude Code and AI agents.

Enables Claude, Cursor, and other MCP clients to audit public sites across six areas, check and follow llms.txt links, compare rival sites side by side, and run sixteen content gates on local Markdown or MDX using only free public signals.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Checks MCP servers by URL, npm package, or official MCP Registry search, reporting availability, response speed, exposed tools, resources, and prompts. It flags changes since prior runs as breaking, warning, or info using saved snapshots.

Enables interaction with Webhook.site to create, manage, and monitor endpoints for capturing HTTP requests, emails, and DNS lookups. It provides 16 tools for testing webhooks and inspecting incoming data through the Model Context Protocol.

Programmatic email deliverability testing for AI agents. Create inbox placement tests across Gmail, Outlook, Yahoo, Mail.ru, Yandex — get per-provider placement (Inbox/Spam/Promotions), SPF/DKIM/DMARC auth, Rspamd & SpamAssassin verdicts, DNS health (MX, PTR, DNSBL), and live SSE results.

Windows桌面截图与窗口自动化工具，支持后台操作、不抢焦点，可截取窗口/屏幕、模拟鼠标键盘、点击菜单等。

MCP server with seven website-audit tools (incl. pay-transparency posting monitor) for AI agents: schema/AI-readiness, WCAG accessibility, tech stack, PageSpeed, AI-crawler access, page-change monitoring. Runs public Apify Actors on the user's own Apify account; MIT licensed.

Enables engineering leaders to query their AI assistant for quality KPIs — MTTR, MTTA, DORA metrics, defect escape rate and SLA breaches — computed from Jira bug, incident and deployment CSV exports already on hand. It delivers trends, period comparisons, hotspot findings and ready-to-share executive summaries and HTML dashboards without a BI stack or data warehouse.

Gives AI assistants structured access to a test automation stack so they can run suites, inspect failure details and captured output, look up test data, and file Jira bugs (or drafts) only for confirmed defects. It works with any framework that emits JUnit XML, letting assistants triage flaky, environment, and test-data issues alongside real product defects.

Gives Claude Code a live Node.js debugger over the V8 Inspector protocol, letting it launch or attach to a running process, set conditional breakpoints, step through execution, inspect scopes and stack frames, evaluate expressions in a frame, and read stdout/stderr — with no instrumentation added to the code.

Gives AI coding agents a ten-phase frontend design pipeline (audit, style direction, build, review, verify, perf, brand) with an exclusive style menu. Its verify_page tool loads a URL in a real Playwright browser at mobile and desktop viewports and returns screenshots, ARIA snapshots, console errors, failed requests, horizontal-overflow offenders, axe-core violations, and pointer type.

Enables AI assistants to query the Google Play Developer Reporting API for Android production stability data, including crash and ANR issue clusters, de-obfuscated stack traces, release track discovery, startup and crash metric trends, version-to-version comparisons, and anomaly alerts. It also provides guided prompts for incident triage, baseline profile verification, and weekly stability reporting with built-in token compression and caching.

Orchestrates full-stack frontend engineering—from intent discovery and design genome generation to automated multi-viewport QA—through JSON-RPC tools for AI agents.

Enables out-of-band interaction testing by integrating ProjectDiscovery's interactsh service as an MCP server. Allows AI agents to create callback domains, send probes, and capture DNS/HTTP interactions for security testing and verification workflows.

Enables debugging of Python, JavaScript/TypeScript, Go, and Rust code using real debuggers with breakpoints, stepping, variable inspection, and stack trace navigation through the Debug Adapter Protocol.

Enables coding agents to use a real debugger (Python via debugpy) for launching, attaching, setting breakpoints, stepping through code, inspecting stack frames, and evaluating expressions through MCP tools.

Enables converting Python tracebacks into root-cause analysis and runnable unittest/pytest suites, including edge-case generation and assertion auditing. It provides local MCP tools for decompiling stack traces, generating tests, and reviewing test coverage gaps.

Enables desktop FDM 3D-printer users to deterministically estimate filament, time, energy, and cost; assess cantilever and container viability; compare failure costs; plan unattended print start times; and decide whether off-design or repaired parts should be kept, tested, or reprinted.

Integrates Slack channels and Slack Lists to automate bug report scanning and QA status management. It enables users to retrieve bug details, update list items, and post fix updates directly within Slack.

Enables AI agents to run deterministic, fully local static security and compliance audits of code, configurations, and dependencies through 32 MCP tools covering 24 vulnerability categories. It also detects the project stack, generates risk reports and checklists, suggests stack-aware remediations, and re-validates fixes to confirm vulnerabilities were eliminated.

Enables AI assistants to debug PHP applications through Xdebug's DBGp protocol, providing full debugging control including breakpoints, variable inspection, stack traces, and advanced features like watch expressions and code coverage.

Enables controlling and inspecting a running Dolphin emulator through its Debug Adapter Protocol, with persistent connections, task-oriented tools, and a raw request escape hatch for execution, breakpoints, stack/variables, memory, disassembly, sources, watches, scans, and code injection.

Automates Android Virtual Device operations by enabling users to start emulators, execute development commands, and capture screenshots. It facilitates Android testing workflows by returning command outputs and visual feedback directly to AI assistants.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

Validates .mcp.json and claude_desktop_config files to catch duplicate server names, missing binaries, invalid environment variables, and unreachable endpoints. Reports each problem along with how to fix it, so silently failing MCP servers can be diagnosed and repaired.

Maps Electron app IPC handlers to MCP tools and provides 33 built-in CDP tools for DOM automation, screenshots, interaction, and debugging, enabling Claude Code to directly control your Electron app.

Analyzes application crash dumps and extracts debug symbols, transforming Windows minidump files into readable stack traces and providing AI-powered crash analysis to help identify root causes and fix critical issues.

Provides production-grade starter templates for MCP servers with permission boundaries, integration tests, and eval contracts, enabling rapid development of secure and testable MCP servers.

Wraps the Judge0 evaluation stack into a local algorithm-problem judging service that exposes seven tools to MCP clients, enabling models to browse problems, self-test against public samples, and submit solutions for full hidden-test grading. Judging parameters, submission limits, and hidden-data access are enforced server-side so models cannot bypass them.

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

Enables coding agents to deterministically audit markup and stylesheets for WCAG contrast failures, accessibility errors and design-token sprawl, and to compute contrast ratios and inventory hardcoded colors, sizes, radii and spacing before shipping UI code. Works across any web stack with Claude Code, Claude Desktop, Codex and Antigravity, and can also run as a CI check that exits non-zero on errors.

MCP server for debugging Unity Editor managed code, supporting attach, breakpoints, stepping, call stack inspection, and read-only evaluation.

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

Full-stack security for AI agents — static analysis + MCP runtime interception. 31 rules detect prompt injection, data exfiltration, backdoors, tool poisoning, and cross-file attack chains. Includes MCP proxy for real-time blocking, Python AST taint tracking, multi-language injection detection (8 languages), and AI-powered deep analysis. Free, offline, zero-config.

Enables MCP clients to retrieve lab performance scores and field Core Web Vitals data for web pages via Google APIs.

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.

Automated testing tool for MCP servers that checks protocol conformance, tool quality, and reliability, providing a scorecard with spec citations and non-zero exit codes for CI.