"Understanding and Using JSON" matching MCP servers:

Enables AI assistants to identify and quantify value leaks in organizations using Melt's methodology, providing structured estimates instead of generic vendor lists.

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

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.

Provides market-intelligence tools for researching AI employee opportunities across Egypt, Saudi Arabia, and UAE using Meta ads evidence, with capabilities for competitor analysis, comparison, and evidence retrieval.

MCP server that provides demand-signal scanning tools from GitHub, Hacker News, npm, and PyPI to validate digital-product and dev-tool ideas. Enables agents to query repo signals, trends, and download counts via stdio JSON-RPC.

An MCP server that enables AI tools to interact with the Lalaleap project management system using natural language, allowing creation and management of requirements, bugs, todos, and projects.

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.

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.

MCP server that enables structured decision-making using weighted decision matrices, sensitivity analysis, and robust recommendations.

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

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

Enables AI systems to generate detailed, well-structured Product Requirements Documents (PRDs) using various AI providers or templates through the Model Context Protocol.

MCP server for product analytics using dot plots to visualize individual user activity, with tools for user classification, aha moment detection, retention analysis, and generating shareable HTML reports.

MCP server that lets product managers turn ideas into editable Figma designs using natural language, with explicit planning and approval before any changes.

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.

Gives Claude four decision tools for product management: rank backlog, mine customer feedback, size sprint capacity, and trace dependency risk, all grounded in JSON data.

A local MCP server for Jira Server/Data Center that enables Claude and Codex to manage issues, comments, transitions, worklogs, and links through the Jira REST API v2 using Basic Auth.

Enables users to clarify vague goals through Socratic questioning and generate structured technical specifications, using a curated knowledge base and session state without external LLM APIs.

Provides Claude with tools to make Ship/Delay/Kill recommendations on feature requests using product metrics, roadmap, and OKRs.

Enables stdio-based MCP clients to read and write the Propyte Mejoras IA board on hub.propyte.com by translating JSON-RPC stdio messages into authenticated HTTPS requests to the Hub.