"Retrieve Page Information from a Specific URL" matching MCP servers:

Enables agents to fetch app reviews from Apple's App Store and Google Play per country store, newest first, filtered by date or star rating, with each scheduled run returning only reviews not seen before. It also returns a per-app, per-country rating trend row with the current rating, rating count, change since the last run, and the star split of new reviews.

Enables fetching daily Product Hunt launches as dataset rows with maker names, product website and company domain, X and LinkedIn links, votes, rank, and topics. Users can filter by specific days or days back, topics, keywords, and minimum votes, or skip already-seen launches on a schedule.

Read Layerdock website feedback in your coding agent. Each pin comes with the CSS selector, screenshot, page URL and the console and network trace captured when the client reported it. Fix the bug, reply on the thread and mark it resolved without leaving the editor. Works with Claude Code, Cursor, Codex CLI, Windsurf, VS Code, Zed and Antigravity.

Feedback collection for AI agents. Create surveys from JSON schema, collect responses from groups of people, and retrieve structured results.

Enables discovery of a company's public feature board (Canny, Featurebase, Frill, UserVoice, Productboard) from its domain and retrieval of its most-voted requests with votes, status, comments, and the integrations customers ask for. Supports monitoring runs that surface only new requests, status changes, and vote jumps since a prior run using a saved state key.

Enables AI assistants to search and retrieve verified software comparisons, including current pricing with source and date, and ranked alternatives for software switching decisions.

Enables AI clients to create AI-powered forms from natural language descriptions, preview, publish, and close them, and then list, summarize, and export collected responses as CSV. It ships nine tools so an agent can run a form end to end.

Enables fair random picks, team splits and wheel spins inside chat assistants and other MCP clients, with results shown in chat. It also runs certified draws that seal the winner before the spin and publish a verifiable public proof page, so anyone can check the outcome.

Enables AI assistants to search new software launches, read product pages, find alternatives, and follow daily rankings from inside a chat using public read-only data.

Deterministic marketing math for AI agents: 62 engines that compute LTV:CAC, CAC payback, pipeline coverage, A/B test significance, budget allocation, pricing and retention from your own numbers. Runs locally with Node 18+, makes no network calls and stores no data.

Enables AI agents to query real-time SaaS pricing tiers, compare developer tools side-by-side, and retrieve recent product changelogs.

re-backlog idea management with decision tracking, signal aggregation, and RICE scoring. Captures product feedback from Slack, Teams, Discord, and GitHub

Enables coding agents to read feedback left on a running app—including the thread, page, pointed-at element, and screenshots—and to report back, manage feedback items, and publish review links.

Enables AI assistants to retrieve a Wildberries seller's product cards and customer reviews through the official Content and Feedbacks APIs.

MCP server for interacting with Shortcut (project management) directly from Claude, enabling viewing, searching, and creating stories, epics, and objectives with safe read-only and create operations.

Stop your AI agent from rebuilding what already exists. MCP server that checks GitHub, npm and Python repos for maintained alternatives before you scaffold.

Model Context Protocol server that enables AI assistants to query and analyze project feedback and bug reports from FeedbackBasket projects, with filtering by category, status, sentiment, and search capabilities.

Enables agents to create and drive instrumented Design Thinking sessions, inspect the append-only journal, and retrieve dossiers and handoff specs through MCP tools and resources.

Guides product design through mandatory phases—discovery, definition, prioritization, flows, and verification—so an AI assistant can run a project from problem to verified scope, with strict or free progression modes.

Run real user interviews from AI agents and retrieve structured insights with themes and verbatim quotes.