"Buy Me A Coffee" matching MCP servers:

Inline review comments for markdown specs and design docs. Agents request human review mid-task via MCP and pause until you send feedback.

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.

MCP server bridging product ideas to prioritized roadmaps + spec drafts. Hands directly off to mk-spec-master. (AI 規劃大師)

MCP server for AI agents providing 300 curated Japanese home & lifestyle products across 31 categories. Features mm-precision dimension search, related-item chains (1 product → 3-5 accessories), shelf+storage coordination, and Rakuten/Amazon affiliate links.

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.

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

Unofficial MCP server for self-hosted Plane Community Edition, enabling project management tasks like managing work items, cycles, modules, and initiatives via natural language.

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

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.

Remote MCP server for Pruva, enabling AI assistants to manage products, features, and documents over HTTPS with OAuth authentication.

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

Connects AI agents to the Outset research platform for creating studies, managing recruitment, and querying results via the Model Context Protocol.

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 search Sunex's lens and imager catalog using natural language queries. It provides tools for finding compatible lenses, sensor specifications, and product details through a public Model Context Protocol server.

Gives AI agents governed, read-only access to product marketing intelligence such as positioning, messaging, win/loss, and competitive briefs, plus persona-based draft reviews.

Enables AI agents to read-only access business intelligence in a Revinho workspace, combining Google Analytics 4, Stripe, and optionally Google Search Console to answer questions about changes, performance, revenue, underperformance, and opportunities.

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.

A demo MCP server exposing an in-memory todo list via tools (add_task, list_tasks, complete_task), a resource, and a prompt, over both stdio and HTTP transports.

Enables AI applications to query customer feedback, customer profiles, and analytics through the Medallia Query API.

Transforms raw business ideas into complete product strategies by orchestrating a specialized AI team for market research, competitive analysis, persona building, and financial modeling, culminating in a pitch-ready presentation.

Enables AI assistants to interact with RetainQ workspaces, including querying retention metrics, managing flows, analyzing churn data, and handling customers, offers, opportunities, and feedback.

A task-management MCP server that enforces strict gates on task states, WIP limits, and closing reasons, refusing operations that would leave the list ambiguous or rotting.

A production-ready MCP server enabling AI assistants to interact with Jira projects, issues, sprints, users, attachments, and worklogs via natural language, with support for both local stdio and remote HTTP transports, dual authentication methods, and enterprise-grade security controls.

Enables AI agents to run a full QFD session over the Model Context Protocol: build and edit a House of Quality (stakeholder needs, measurable characteristics, relationship cells, trade-off roof, work items), validate it, and read back priorities, gates, the diagonal build order and item progress. It also exports the house as TSV, CSV, JSON, SVG or an Excel workbook, with optional file persistence so a person can keep editing where the agent left off.

Enables an AI agent to audit a website for launch readiness, find suitable startup/SaaS/AI directories using filters like DR and link type, and submit product listings to SaaSCity as drafts.

A single source of truth for a product's state, roadmap, and deferred work, read and written equally by you and your AI agent via web dashboard and MCP.

Remote MCP server that lets AI assistants read tracked competitive intelligence — competitor news, reviews, website changes, social posts and ads — and act on it by managing projects, competitors, battle cards, reports and canvases. Every answer carries a source and a date, and write actions only publish when explicitly shared.

Turns product ideas into implementation-ready planning packages including PRD, requirements, user flow, wireframes, data schema, API contracts, and SDK boundaries, with an MCP handoff for coding agents.

An MCP server that provides a drag-and-drop UI for prioritizing list items, allowing users to reorder items interactively and send the prioritized list back to the chat. It supports MCP Apps UI rendering and falls back to text instructions for hosts without UI support.