"A guide to deploying a web application" matching MCP servers:

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 searching and retrieving content, module information, and Twig template snippets from a-blog cms via its GET API.

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

Enables web research through multi-provider search, documentation lookup, public code search, and clean Markdown extraction from static or JavaScript-rendered pages via five read-only tools.

Ultra-lightweight headless browser for AI agents. Provides MCP tools for navigating URLs, extracting structured content, and building autonomous agent workflows.

MCP server that parses web pages into clean markdown via Crawl4AI's HTTP API, exposing parse_page and health-check tools with configurable proxy and timeout.

Enables AI assistants to fetch public web pages as readable text or HTML, with caching, live refresh, and parallel job handling.

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

This MCP server lets AI agents connect to Figma to read, analyze, create, and modify designs, including elements, styles, assets, and code generation. It works with free Figma accounts and supports parallel multi-agent use across MCP-compatible clients.

AI job search and job application tracker. Search live jobs by title, location, salary, date posted, and remote or on-site, and see each one checked on real data: pay against market rates, ghost-job signals, visa sponsorship history, and how well it matches your resume. Check how hiring software reads your resume, track applications, set follow-up reminders, and get email alerts.

Enables coding agents to drive a real Playwright-backed browser session, navigating, observing, interacting, and capturing proof on web pages via MCP tools across stdio and Streamable HTTP transports.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

What is PandaDoc MCP? PandaDoc MCP (Model Context Protocol) server connects AI agents directly to PandaDoc's document workflow — creating, sending, and tracking contracts and agreements without leaving your AI client. How to use PandaDoc MCP? To use PandaDoc MCP, connect your PandaDoc account, provide the required API credentials, and configure the appropriate MCP client.

Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.

Enables autonomous agents to inspect live web pages, prune noisy DOM content down to semantic essentials, and bind each form field to agent state by matching input attributes, autocomplete hints, and regex validations. It also converts HTML/Markdown tables into type-inferred JSON records and scores agent action trajectories for precision, recall, and edit distance.

Enables autonomous web form inspection and schema auto-mapping by matching input attributes, autocomplete hints, and regex validations to agent state, while also pruning DOMs and converting tables into structured JSON for web agents.

Provides Figma design context to AI agents, enabling code generation, design system integration, and canvas manipulation through MCP.

Enables MCP-compatible agents to scrape web pages and distill them into clean Markdown by removing cookie banners, navbars, and ads for efficient consumption.

Connects AI coding assistants to Anima Playground, Figma designs, and design systems, enabling handoff of designs to production code.