"Retrieving Markdown Files from a Database" matching MCP servers:

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.

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

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.

Feedback collection for AI agents. Create surveys from JSON schema, collect responses from groups of people, and retrieve structured results.

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 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.

A Jira-lite MCP server that enables planning-first workflows for AI-assisted development. It manages tickets as markdown files with YAML frontmatter, guiding agents through epics, stories, tasks, and architecture decision records.

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.

An MCP server for Capacities API 2.0 that enables AI agents to discover, create, read, update, and delete objects, blocks, and daily notes with structured JSON or Markdown, using personal API tokens.

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.

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.

MCP server for sharing markdown/text as beautiful, permanent web pages via plsreadme, with tools to share files/text, update and delete docs, and list previously shared links.

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.

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

Connects Huawei AppGallery Connect operational data to any MCP-capable AI client, letting users analyze report trends across 16 report types, crash and AppFreeze metrics, review and rating insights, and generate Markdown daily or weekly digests. All 13 tools are strictly read-only, run with zero dependencies, and keep credentials in memory only.

Enables a coding agent to keep one JSON feature specification per feature, governed by a progressive schema, and to render it as editable Markdown documents for a product owner. Answers and direct Markdown edits are reconciled back into the validated model through stdio MCP tools, applying coherent changes and reporting those that widen scope or contradict the model.

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

Enables coding agents to generate software engineering documents — PRD, SRS, ERD, SDD, design spec, traceability matrix, and agent instructions — as Markdown so they build from written specifications instead of guessing. Unsupplied inputs are marked [NOT SUPPLIED] or UNMAPPED rather than filled with invented data.

Enables AI assistants to manage Customer Thermometer surveys, including listing thermometers and recipient lists, adding or unsubscribing recipients, sending email surveys, and retrieving NPS, happiness, response, and comment reports.

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.

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 query and segment event data from Amplitude Analytics via the MCP protocol.

Enables agents to create tier-grading collections, upload files or text items, generate per-person ranking links, and read back individual and averaged S/A/B/C/D/F placements and results over a stateless streamable HTTP endpoint.

Build surveys, read responses and make reports in SurveyNinja from your AI assistant. Describe a survey in plain words, check it before launch and publish it, then read the responses, get a summary or an AI report and export the results to CSV, Excel, SPSS or PDF. Hosted server, sign in with OAuth or an API key; works on every plan, Free included.

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