"SearXNG - Open Source Metasearch Engine" matching MCP servers:

An AI-native product development pipeline that guides users from idea to shipping with structured research, architecture, build, validation, and traceability using 52 tools across 4 domains.

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

Provides MCP tools for AI agents to create and read decision decks, propose cards and build plans, and retrieve finished tasks, enabling local decision review workflows with portable task handoffs.

Agentic pipeline that transforms ideas to revenue — for solo founders and bootstrappers.

Manages and authoring of business rules in a scenario studio tool, enabling agents to read, update, link rules, and assign them to capabilities via MCP tools.

Enables AI assistants to search and retrieve verified software comparisons, including current pricing with source and date, and ranked alternatives for software switching decisions.

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.

Open source MCP server for OpenProject aimed at community edition users

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.

This MCP server enables users to keep one source of truth for their experience and generate role-tailored, ATS-safe PDF and DOCX CVs, with enforced skill levels, job-post gap reports, and build verification.

Turns product feedback pinned to a live UI into an actionable backlog for Claude Code — list comments, open one with its target element's HTML, computed styles and screenshot, and update its status.

Mogut — open-source, read-only MCP server for Apple App Store ASO and growth intelligence: keyword research, competitor analysis, review mining, pricing, localization, and reports.

Provides a transparent, deterministic multi-criteria decision analysis engine that ranks options against weighted criteria with exact, explainable results.

Enables customer-success teams and AI hosts to inspect account evidence, assess onboarding readiness, assemble unsent bilingual follow-up drafts, and prepare traceable technical handoffs through scoped MCP tools. It preserves source references and distinguishes recorded failures, missing evidence, and possible explanations for human review.

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

TruePPM MCP servers ask real questions of your self-hosted TruePPM instance: the critical path, a Monte Carlo slip forecast, sprint status, the risk register, My Work. Answers are computed server-side by the same CPM/Monte Carlo engine the web UI uses, never guessed by a model, and nothing leaves your box.

Enables Claude Code to run multi-round grilling sessions in a browser UI, publishing rounds of questions with options and recommendations that users answer with buttons or free text. Supports per-question asides (re-pitch, visual diagram, plain-language explainer), chat notes between rounds, and a polling contract so sessions can stay open without blocking the client.

An MCP server that exposes Google Play Console analytics to AI agents, including store-listing acquisitions by traffic source, installs, uninstalls, ratings, crashes, subscriptions, and reviews by reading bulk CSV reports from Cloud Storage.

Enables keyword search across source-traceable evidence chunks, listing opportunities linked to supporting evidence, and deterministic ROI calculations with model-proposed inputs and TypeScript-computed dollar figures.

Lets AI clients draft, inspect, edit and review wireframe/storyboard boards over MCP, sharing one live board with the app the user has open. It also supports loading boards back, resolving review notes, and exporting realistic previews so specs can be iterated from natural language.

Enables users to transform raw multi-source enterprise findings into complete, structured strategic documents, auto-generating executive summaries, risk registers, decision matrices, and appendix citations. It runs as a zero-dependency Python MCP server that works with Claude Desktop, Cursor, and other MCP-compatible clients.

Enables auditing AI products against a 12-criteria human-impact rubric and generating 0–100 scores, evidence-backed or provisional badges, and shareable teardowns from structured findings and source URLs.

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.

Enables agents to discover and validate SaaS opportunities mined from real user complaints across eight public sources, scoring ideas for commercial intent. Also allows fetching detailed dossiers on specific gaps with source complaints, MVP scope, pricing, competitors, and risks.

Turns the five Playing to Win boxes into queryable data over MCP: list all positions, open one position with its supporting, opposing, and complicating evidence, adopt or reject it with a note, record new evidence, add positions into a box, and organize several cascades of boxes. Read and write tools share the same core and query layer as the CLI and web pages, so every surface stays consistent.

Turns a rambling app idea into a structured planning canvas of screens, flows, systems, data and open questions inside an existing Claude Code or Codex session, then writes the settled plan back to that session as a spec to build from. Plans are stored in the project's .doan/ folder alongside the code.

MCP server for Open Feedback, enabling AI assistants to submit, list, get, update status of, and analyze product feedback via a local HTTP API and JSONL storage.

Enables AI assistants such as Claude, Cursor, and Codex to pull structured, source-backed, dated competitive intelligence — positioning, pricing, ads, social, reviews, hiring, momentum, and exploitable weaknesses — for any company in the universe, alongside the user's own projects, reports, battlecards, timelines, and evidence. Read-only by default, with optional opt-in competitor creation and plan-gated tools for deeper analysis.

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.