"stack and dns mcp" matching MCP servers:

Enables agents to explore the Slack SDK's allowed methods via TypeScript signatures and then run JavaScript that chains multiple Slack Web API calls in a single sandboxed execution. The isolate holds no network access or Slack token, since credentialed calls are brokered by the host.

Enables AI agents to debug embedded systems by providing a comprehensive interface for GDB operations across multiple architectures like ARM and x86. It supports remote debugging via gdbserver or QEMU, allowing for detailed inspection of memory, registers, stack frames, and variables.

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.

An MCP server that enables AI agents to debug .NET applications using netcoredbg. It supports core debugging tasks like setting breakpoints, stepping through code, and inspecting variables or stack traces.

Self-hosted Docker sandboxes for coding agents: create a sandbox with its own Docker daemon, deploy a docker-compose stack into it, read and write its files, and get a URL for every app. Tokens can be limited to one sandbox.

Enables AI assistants to generate, edit, and deploy production-ready full-stack applications through VULK's automated development pipeline. It supports multi-file code generation, browser verification, and exports to web, PWA, and mobile platforms.

Enables Claude to function as a full-stack software engineer with comprehensive development capabilities including project creation, database management, frontend/backend development, testing, deployment, and DevOps operations across multiple frameworks and technologies.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

MCP server that detects project frameworks (25+), starts dev servers, and creates public Cloudflare tunnels from a single tool call. Text Claude via Dispatch "start my project and give me a link" and get back an HTTPS URL you can open on your phone. Supports full-stack projects, auto-installs dependencies, and also works as a standalone CLI.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

Enables AI assistants to automate Next.js development including project scaffolding, React component generation, API route creation, and full-stack application workflows with TypeScript and Tailwind CSS support.

Enables MCP-capable AI agents to work on bounded project folders through read, write, run, and verify tools, with durable approval/resume workflows and completion proven by observed verification evidence rather than self-reported results.

A unified MCP server providing AI agents with 40+ developer APIs including geolocation, crypto prices, DNS lookup, and web scraping. Enables natural language access to various tools through a single gateway.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Provides production-ready implementation patterns for features like authentication, payments, and deployments to AI coding assistants. It enables developers to search and retrieve battle-tested code snippets and setup steps for full-stack tasks.

A full-stack API automation testing server that parses OpenAPI/Swagger/Postman/HAR specs, generates comprehensive test scenarios and executable code, and provides AI-powered review and auto-fix.

Provides AI-powered assistance for coding problems using Google's Gemini AI, combined with Perplexity insights and Stack Overflow references, facilitating contextual analysis and automatic response archiving for improved troubleshooting.

Enables offline AI agent automation with embedded local LLM (Qwen 2.5), sandboxed file operations through AgentFS, and dynamic skill loading. Exposes capabilities via MCP with tri-state safety guards for private, air-gapped environments without network connectivity or API costs.

A multi-agent system that connects a math server and weather server, allowing users to perform mathematical operations and retrieve weather information based on the numerical results.

A Cloudflare Workers-based MCP server template for building remote MCP servers without authentication. Provides a starting point for deploying custom tools accessible from Claude Desktop or other MCP clients.

Provides 8 specialized AI agents (Frontend, Backend, Database, API, DevOps, Testing, Security, Performance) for full-stack web development, powered by high-speed Cerebras inference and integrated with OpenCode CLI.

A Model Context Protocol server that enables natural language querying of the Censys Search API for domain, IP, and FQDN reconnaissance, providing information about hosts, DNS, certificates, and services in real-time.

Hardened MCP bridge to the full Ollama ecosystem — local and cloud models. 6 tools covering health, model management, chat, and generation with SSRF mitigation, singleton HTTP client, and structured error handling. Two deps, one file.

Composes arbitrary MCP tools using JavaScript, enabling discovery, composition, and execution of other MCP servers through a single 'exec' tool.

A persistent Python REPL MCP server for AI agents with stateful execution, real timeouts, crash isolation, and an MCP bridge to call other tools in the project.

MCP server that gives AI assistants a secure gVisor-isolated sandbox to run Python or JavaScript code, returning stdout, stderr, and exit codes without local access.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Enables running one Codex CLI task at a time in an isolated temporary folder, returning Codex's answer and a manifest of produced files for manual review and selective copying.

A code-execution MCP server for Klaviyo and Shopify analytics, enabling safe JavaScript execution in a V8 sandbox with state persistence across calls.

Enables AI assistants to run code in 24 programming languages on HackerEarth's sandboxed servers and read the printed output or errors, without installing local toolchains. It also lists supported languages with exact identifiers for use when submitting code.

Enables MCP-compatible AI assistants to run Python code on Google Colab GPU/TPU runtimes, supporting accelerators like T4, A100, H100, with background execution and Google Drive integration.

Enables a model to write short Python programs that call a wrapped MCP server's tools as async functions, rather than invoking them one at a time. The program runs in a locked-down Docker sandbox, and every tool call passes through a host-side bridge that enforces an allowlist, schema-validated arguments, call budgets, timeouts and output caps.

An MCP bridge that lets Codex supervise Reasonix as an implementation worker in an isolated Git worktree, ensuring safe delegation with scoped writes and sandboxed execution.

Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.

Enables AI agents to interact with Jupyter notebooks via MCP tools for querying, modifying, executing, and setting up notebooks, with state preservation and real-time collaboration.

Enables MCP clients to execute Python code directly via stdin, preserving quotes, f-strings, and backslashes exactly as written, and returns stdout, stderr, and exit codes.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

MCP server that reviews and fixes code across four dimensions (security, standards, production readiness, sustainability), runs tests to verify fixes, and provides findings with diffs.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.