"New product development resources and information" matching MCP servers:

Enables AI agents to track and manage product development projects through structured 7-phase lifecycles with sprint tracking, role-based collaboration, and multi-project support. Provides phase management, progress tracking, and team coordination tools for complete product development workflows.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables autonomous creator operations and influencer marketing automation by dispatching product seeding kits and coordinating unboxing workflows through MCP-compliant clients.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

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.

An MCP server implementation that leverages Google's Gemini API to provide analytical problem-solving capabilities through sequential thinking steps without code generation.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.

A comprehensive framework for building reliable Python MCP servers that enforces incremental development practices through Cursor Rules, featuring built-in security measures and a smart rule system.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

An MCP-based trivia game server where AI creates rounds with two true statements and one false 'twist' about various topics, letting players guess which statement is false.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Enables AI agents to claim, heartbeat, submit, split, park, and resume atomic work-queue tasks, with fencing tokens and receipts, while storing and versioning records and content-addressed files through MCP.

Enables enterprise MCP clients and partner agents to execute tenant-isolated underwriting, claims, and compliance workflows with durable step checkpointing, evidence-grounded tool calls, and deterministic PDF citation receipts. It supports A2A task dispatch and MCP JSON-RPC operations while blocking cross-tenant access and ungrounded deliverables.

Enables Grok Bots to install, verify, build, seal, unseal, list, and update hash-pinned skill packs from static URLs, expanding skill capacity beyond template limits without executing pack content.

Enables MCP agents to play chess locally, supporting join, state, legal moves, and move execution with per-session color ownership.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

MCP server that exposes Gmail and SAP tools, enabling natural-language email management and SAP data browsing through a Mistral-powered AI assistant.

Provides plan state management and phase gate enforcement for AI development loops. It tracks task progress and coordinates the lifecycle of agents by requiring specific evidence before advancing through development phases.

Enables AI agents to dispatch human verifiers for physical world tasks like product authentication, property inspection, and document verification, returning timestamped evidence reports.

Provides comprehensive tools for React Native development, automating project initialization, version management, upgrades, Expo integration, and development workflows through AI assistance.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

An MCP server that automates the full software development lifecycle through an AI-driven TDD state machine. It handles everything from task decomposition and test-driven development to integration testing and automated pull request creation.

Enables AI agents to access a collection of specialized agent instructions on demand, covering categories like tech, business, growth, and product.

Enables MCP clients to start new Codex conversations and continue existing threads, exposing codex and codex-reply tools over stdio via the Codex app-server protocol.

Streamlines development workflows through AI-assisted codebase analysis, comprehensive planning, task breakdown with dependencies, and automated implementation verification. Enables systematic approach to complex development tasks like framework migrations and feature implementation.

A meta-MCP server that helps users create new MCP servers through AI guidance, templates, and streamlined workflows, transforming ideas into production-ready implementations with minimal effort.

A durable multi-agent orchestrator for software development with explicit run graphs, checkpoint/resume capabilities, and project memory exposed through MCP resources and tools. It enables coordinated agent workflows for coding, review, repair, CI, and approval with SQLite-backed memory retrieval and pluggable research backends.

An intelligent tool that automates the setup of new Model Context Protocol (MCP) server projects through a conversational interface. It generates project structures, technical specifications, and context-rich documentation to streamline AI-assisted development in TypeScript or Python.