"Searching for publications by user 'modelcontextprotocol' on MCP" 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.

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.

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.

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.

Enables creating, managing, and analyzing live audience surveys on rifts.to directly from an AI client. Supports creating surveys, listing them, retrieving results, and closing surveys via natural language.

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.

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 natural language control of OmniPlan 4 on macOS, allowing creation and manipulation of tasks, resources, dependencies, and project metadata through MCP-compatible agents.

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.

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.

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.

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.

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.

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.

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

Enables customer feedback management by integrating with Canny.io, supporting board listing, post retrieval, search, and creation.

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

Provides a governed analytics layer over synthetic product data, exposing MCP tools for investigating product metrics, slicing by dimensions, and comparing periods while preventing unrestricted SQL access.

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.

Automates Cole Medin's context engineering methodology to generate high-quality Product Requirements Prompts (PRPs) by analyzing existing codebases, asking contextual questions, and synthesizing insights.

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.

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.

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.

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.