"NestJS Framework for Building Backend Applications" matching MCP servers:

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.

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.

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.

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.

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.

Official MCP server for FeatureJet, enabling agents to read and act on customer feedback boards: list/search posts, file feature requests, and pull top-voted planned items.

MCP server for managing Tokei pre-launch and waitlist campaigns, wrapping the Tokei v1 REST API. Enables querying pages, stats, leaderboards, signups, and performing write operations like cloning pages and uploading media through natural language.

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.

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.

MCP server for noise-reduced X (Twitter) search planning and Feedback-to-Fix engineering handoffs. Enables AI agents to generate structured search plans, filter signals, and build handoff packages via MCP tools.

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 guide users through pretotyping, turning raw ideas into Lean Canvases, testable hypotheses, and small experiments to validate demand before building.

Headless MCP server for Simpson Strong-Tie product data. Provides AI agents with structural connector lookups, load calculations, code compliance verification, and engineering specifications.

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.

Enables users to create, rank, preview, and download tier lists entirely within chat, including custom tiers, embedded logos, and PNG exports. Board state is maintained conversationally for stateless, private collaboration.

Enables interaction with Posthog through a standardized MCP interface, providing access to Posthog's tools and services for analytics and product management.

Enables AI agents to interact with Plane project management APIs, offering tools for managing projects, work items, cycles, modules, initiatives, and more through MCP.

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.

Enables prioritization of backlog items, analysis of customer feedback, capacity assessment, and dependency mapping for product management.