"A JavaScript library for building user interfaces (React)" matching MCP servers:

Enables conversion of business briefs or authenticated saved projects into structured handoffs for the Lovable plugin, with both anonymous basic mode and authenticated full mode including knowledge base, SEO, WCAG, security, and roadmap guidance.

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.

MCP server that synthesizes user feedback from multiple sources into actionable insights. Aggregates reviews, support tickets, and survey responses to identify patterns and priorities.

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.

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 validate drafted user stories against INVEST and acceptance-criteria rules, suggest splits for oversized stories, and export finished backlogs to Markdown, CSV, or Jira CSV.

Enables AI coding agents to inspect user feedback on live web apps, read full reports and recording timelines, update status, and reply in threads so fixes can be checked and confirmed by reporters.

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.

Creates synthetic user profiles from research data to validate service designs through natural conversation.

Enables agents to securely collect and submit user feedback (voices) into VOD, with per-submission confirmation, batch size limits, host-identity sourcing, and declarative guardrails that block database access and credential reads. Also exposes read-only tools for version info, auth self-check, and reviewing locally submitted entries.

Enables reading release lifecycle status, recent written reviews and bulk monthly report CSVs for allowlisted apps, and searching the full pinned Play API catalog with each method's implementation status. Read-only by design, with keyless authentication, explicit app allowlists and bounded, GET-only access to official Google interfaces.

Enables users to conduct evidence-driven product research through MCP by importing documents, extracting JavaScript code clues, recording cited facts and inferences, tracking validation, managing research lifecycles, and exporting Markdown or JSON reports. It provides 25 MCP tools for turning source materials into traceable research findings and verification plans.

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

MCP server for reading public Featurebase feedback boards, enabling agents to query user feedback, posts, comments, and statistics.

Enables AI agents to generate structured PRDs, decompose epics into testable user stories, validate Given/When/Then acceptance criteria, and export markdown backlogs.

MCP server for product analytics using dot plots to visualize individual user activity, with tools for user classification, aha moment detection, retention analysis, and generating shareable HTML reports.

Captures and organizes requirements as markdown, tracks relationships between personas, requirements, use cases, and user stories.

An AI-powered Product Management Operating System that provides tools for PRDs, user stories, prioritization, competitor analysis, roadmaps, KPIs, meeting notes, and product strategy via MCP clients.

Enables management of Azure DevOps work items including Epics, Features, User Stories, Tasks, and Bugs through natural language. Supports CRUD operations, WIQL queries, work item relationships, and retrieval of project metadata such as iterations and area paths.

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

MCP server for GoalT, a multi-parent value-propagating goal graph. It enables building and querying goal trees, listing priorities, and opening a live dashboard that highlights the active goal during Claude Code sessions.

Official MCP server for FeatureJet, enabling agents to read and act on customer feedback boards: list/search posts, file feature requests, and pull top-voted planned items.

Collect and analyze user feedback from any app via a single API endpoint, with MCP tools for listing, searching, and stats.

Enables AI assistants to query live App Store and Google Play data — keyword demand, rank history, top charts, competitor sets, reviews and ratings — across 100+ countries, directly inside a chat instead of a dashboard. Exposes 35 read-only tools covering app search, ASO keyword profiling, ranking trajectories, and worldwide market comparisons through a remote OAuth-authenticated endpoint.

Enables AI assistants to conduct user research workflows directly from Great Question, including creating studies, recruiting participants, and querying insights.

MCP server for managing Tokei pre-launch and waitlist campaigns, wrapping the Tokei v1 REST API. Enables querying pages, stats, leaderboards, signups, and performing write operations like cloning pages and uploading media through natural language.

A User Story Map and Kanban board that a human and an AI coding agent plan together in real time via MCP, with live bidirectional sync and governance enforcement.

A federated MCP server that combines IBM's engineering-ai-hub tools with its own authoring and orchestration tools for IBM ELM, enabling AI hosts to manage requirements, models, and compliance through a single interface.

Enables external agents to inspect and interact with a local single-project workbench via MCP, providing read-only tools for state, audit, resume, readiness, comparison, impact preview, and repair candidates, while proposals are saved for manual confirmation in the workbench.

Enables AI assistants to conduct research, plan projects, generate PRDs, user stories and task lists, scaffold full-stack starter kits, map codebases, curate context, and execute multi-step development workflows.