"Uber Eats" matching MCP servers:

Enables management of decentralized organizational roles and permissions through Hats Protocol across multiple blockchain networks. Supports creating hierarchical role structures, checking permissions, minting/transferring roles, and analyzing organizational structures through natural language.

Provides an interactive checklist tool that allows AI agents to present step-by-step instructions to users through an automatically opened terminal UI. It enables agents to guide users through manual tasks and wait for completion, skipping, or feedback before proceeding.

Enables AI agents to automatically inspect and fill web application forms on job application sites through a logged-in Chrome, supporting text, select, radio, date, cascading dropdowns, and dynamic row operations.

Simulates harsh, fake user reviews to psychologically condition AI agents for enforcing disciplined development practices, with optional Ollama integration for dynamic criticism.

A Model Context Protocol server demonstrating user management capabilities with tools for creating, retrieving, and generating random user data.

Enables agents to open production change dossiers, attach proof certificates, and request human approval through a secure, unforgeable gate—ensuring no irreversible change can proceed until it has been verified against a sandboxed shadow copy.

Enables systematic code investigation using Monte Carlo Tree Search to explore codebases, analyze files, and provide intelligent insights. It features persistent memory and pattern learning for improved investigations.

Enables Codex to clarify requirements via native MCP elicitation controls, supporting single/multiple-choice and free-text questions with recommended answers and a discuss-first option.

Enables dynamic generation of tools based on user-specific permissions from an external API. Users receive personalized tools tailored to their access rights, with intelligent caching and graceful fallback to static tools.

A server that lets AI agents ask users clarifying questions mid-task via a pop-up terminal window, enabling interactive context gathering.