"A GitHub server implementing the Model Context Protocol (MCP)" matching MCP servers:

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.

Enables MCP clients to search and retrieve product management articles, talks, podcasts, related content, live events, and job listings from Mind the Product through read-only tools and resources.

A Python MCP server that gives Claude access to PM workflow data such as sprints, roadmap, blockers, and workload, using synthetic fixture data (no API keys required) and can be forked for real Linear/Jira integration.

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.

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.

A local MCP server that gives AI agents read access to Roblox experience analytics via the Open Cloud API, exposing 168 metrics and analysis tools for retention, engagement, monetization, and more.

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.

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.

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

Enables AI agents to query SensorTower's APIs for mobile app market intelligence, including download and revenue estimates, store leaderboards, App Store featuring and ASO keywords, reviews, Apple Search Ads, and audience insights across roughly 76 endpoints. It normalises the API's unit, shape, and error quirks so results arrive clean, capped for model context, and with actionable error hints.

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

MCP server that provides demand-signal scanning tools from GitHub, Hacker News, npm, and PyPI to validate digital-product and dev-tool ideas. Enables agents to query repo signals, trends, and download counts via stdio JSON-RPC.

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.

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 an agent to run 20 product-management workflows that convert call notes, specs, customer feedback and competitor changes into deduped Linear issues, PRD drafts, roadmap sheets, digests and weekly project updates. Orchestrates only the tools each workflow needs across Granola, Linear, Slack, Notion, GitHub, Stripe, Firecrawl, Tavily and Google Workspace, using credentials kept in the user's own OS keychain.

Enables AI assistants to work with Linear via the Model Context Protocol, covering issue/project management, resource and prompt access, webhooks, and OAuth app setup.

Enables AI assistants to query and segment event data from Amplitude Analytics via the MCP protocol.