Direct Claude integration to access OpenAI chat models with a simple message interface.

A bridge between YouTube API and AI assistants enabling caption download and video summary generation.

Lightweight bidirectional protocol proxy with YAML logging for testing and troubleshooting data flows.

CLI-driven broker enabling discovery, installation, and secure orchestration of MCP components via Docker-based isolation.

Powerful MCP server for managing and serving programming guidelines with markdown rules, flexible storage, and category-based organization.

Summarizes chat messages with queryable results; supports prompts, local chat.db, and Claude Desktop integration for quick deployment.

A comprehensive CLI to organize Claude App components with discovery, auto-install, and interactive configuration for streamlined setup.

Modular, plugin-driven Python toolkit to orchestrate context-aware workflows with clean integration and scalable inter-component communication.

Spinnaker-integrated AI-driven deployment decisions with proactive remediation and context-aware automation.

Notion API integration for Claude to read, update, and manage Notion workspaces with rich block and database operations.

Generates customizable context environments with automated file creation, tool management, and robust debugging for rapid development workflows.

Deep Jira Cloud integration enabling AI assistants to search, create, and analyze issues with rich configurations and workflows.

Minimal Model Context Protocol variant reproducing Claude Desktop JSON Schema validation bugs with complex unions.

A Noko-based integration enabling AI assistants to list, create, and filter time entries, projects, and users with safety-confirmed actions and convenient date presets.

TypeScript tool that auto-discovers, tracks, and manages SQLite databases across a workspace, with real-time monitoring, pooling, and cross-database operations.

In this project, we will explore how the problem of the Multiple Couriers Planning (MCP) is modeled and discuss various approaches to address it: Constraint Programming (CP), Satisfiability Modulo Theories (SMT) and Mixed-Integer Linear Programming (MIP).