"Building and Deploying a React Application with Docker and a Postgres Backend" matching MCP servers:

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

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 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.

An MCP server for accelerator programs to query, review, and manage startup applications via natural language. It enables searching applications by sector or impact score, fetching full application details with reviewer notes, listing pending reviews, and flagging applications for panel discussion.

Connects Claude Code, Codex, Cursor, and other coding agents to a project's idea, feature topology, and ordered task queue over MCP, so they can answer planning jobs, claim, build, and tick off implementation tasks live in your repository, or assemble a shareable PRD.

Enforces a marketing pre-flight: plan the campaign with kill criteria, score each offer on an explainable 0-100 rubric, and require every tactic to cite a scored offer plus a success metric before any spend.

Turns your AI host into a product videographer — scripted screen recordings of your own web app with a gliding cursor, camera zooms, highlight callouts, captions, and branded transitions, plus marketing-grade screenshots. Automatic dark-frame cleanup and MP4/GIF export. Free, MIT, 100% local — no account, no API keys.

It enables solo builders to automatically log product changes to PostHog and later get statistically honest verdicts on whether those changes moved key metrics, without needing enough traffic for A/B tests.

Provides a comprehensive creative thinking tool with methods like structured process, perspective shift, and constraint-based innovation to enhance divergent thinking.

Pre-build reality check for AI coding agents. Scans GitHub, Hacker News, npm, PyPI & Product Hunt in parallel — returns a 0-100 reality signal with real competitor data.

A Model Context Protocol server that provides Monte Carlo forecasting tools for project duration, cost, completion dates, and sensitivity analysis using three-point estimates and historical throughput.

Enables triaging product feedback over a local dataset, including search, theme clustering, severity tagging, and drafting PRD-ready problem statements.

Enables users to describe a product and receive a ranked, live-checked list of directories, launch sites, newsletters, communities, and other submission destinations, with fit scores, pricing, link attributes, and popularity ranks for planning launches.

An MCP server that reads a team's interviews, tickets, and docs to build a model of the product, exposing tools to ask questions, list contradictions and gaps, and draft documents, with all answers citing their source fragments.

Enables agents to evaluate whether a product-adoption message is worth sending by applying a fixed, evidence-based rubric to a draft, recipient context, evidence, and the agent's own assessment, returning a send, revise, or hold recommendation with a score and reasons.

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.

Validates startup ideas with a deterministic scorecard, evidence brief, and verdict before code is written, integrating with MCP-aware build agents to avoid building dead-on-arrival products.

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.

Decision memory for teams building with AI coding agents. Captures every engineering decision once, ranks it by blast radius, and briefs every MCP-compatible agent before it acts, so agents stop making conflicting changes across repos.

Lets an AI agent read a Pyxis dashboard's analytics — overviews, funnels, devices, acquisition, backend requests, visits and project settings — using a read-only access token created by an admin and revocable at any time. It runs locally over stdio, holds no state, and exposes only GET-style reads of the live Pyxis API.

This server assists with A/B testing by calculating required sample sizes and necessary test durations. It provides a suite of significance tests, including z-test, chi-square, and Fisher exact methods, while evaluating the risks of premature peeking.

Provides a transparent, deterministic multi-criteria decision analysis engine that ranks options against weighted criteria with exact, explainable results.

Turns product feedback pinned to a live UI into an actionable backlog for Claude Code — list comments, open one with its target element's HTML, computed styles and screenshot, and update its status.

A Model Context Protocol server for FeaturePulse feedback management, enabling AI assistants to query feature requests, analyze MRR impact, and manage product roadmaps through natural language.

Lets AI assistants query a live feedback collection database in plain language, retrieving entry counts broken down by type and classification status, and listing individual submissions filtered by type, urgency, or sentiment. Access is read-only and privacy-preserving, so answers can be produced without exposing submitter email addresses.

A deterministic what-if scenario simulation MCP server that projects business metrics over time with exact decimal arithmetic, offering sensitivity analysis and break-even solving.

Enables users to stress-test decisions and plans with structured contrarian analysis, surfacing blind spots, hidden assumptions, and failure scenarios through multiple modes such as counter, probe, redteam, and premortem.

Prevents premature AI coding by transforming vague product ideas into structured specifications, architecture decisions, and acceptance criteria through a series of interrogation and compilation tools.

Prioritizes tasks by eliminating distractions, returning a ranked shortlist based on a given goal.

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

A hypothesis-driven B2B positioning engine with 8 tools for strategic positioning, competitive analysis, and go-to-market messaging.

Enables AI assistants to search, de-duplicate and route leads in a demo CRM, with read-only mode and an audit log for accountability.

Enables MCP clients to read and manage Linear issues, sub-issues, relations, comments, projects, cycles, teams, users, labels, and workflow states using a personal API key.

Provides deterministic strategic-planning frameworks as a local MCP server, offering tools like SWOT analysis, marketing mix, lean canvas, and decision matrix without requiring API keys, working offline.

A YouTrack MCP server that enables natural language interaction with your YouTrack instance, supporting issue management, time tracking, agile boards, and more.

A Model Context Protocol (MCP) server that connects Trello to Claude Code. Manage your boards, lists, cards, checklists, and more — all through natural language.

Enables coordinating multiple AI coding sessions by tracking features, decisions, and approval gates in a per-project Postgres database, with atomic IDs and commit-stamped append-only history.

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.

Enables AI assistants to create and validate surveys, distribute them, read and tag responses, and generate reports or exports through a hosted Streamable HTTP MCP endpoint with API-key authentication.