khaoss85/mcp-orchestro
by Various
Transform product ideas into reality with an intelligent orchestration system that bridges Product Managers, Developers, and AI. Orchestro conducts the entire development symphony:
MCP
khaoss85/mcp-orchestro
Added 1 June 2026
Overview
An MCP server that orchestrates AI agents to decompose product ideas into development tasks, track dependencies, learn from patterns, and visualize progress in real time. It uses TypeScript and coordinates between product managers, developers, and AI agents.
Best for
Best for
Developers experimenting with multi-agent orchestration for product development workflows
Use cases
- Breaking down a product spec into granular, ordered development tasks
- Tracking task dependencies and progress across a team of AI agents
- Visualizing the development workflow and identifying bottlenecks
Notes
An MCP server that orchestrates AI agents to decompose product ideas into development tasks, track dependencies, learn from patterns, and visualize progress in real time. It uses TypeScript and coordinates between product managers, developers, and AI agents.
15 stars on GitHub. Last updated 2025-10-13.
Use cases
- Breaking down a product spec into granular, ordered development tasks
- Tracking task dependencies and progress across a team of AI agents
- Visualizing the development workflow and identifying bottlenecks
Pros
- Provides structured task decomposition from high-level ideas
- Includes dependency tracking and real-time progress visualization
- Open source with a lightweight TypeScript codebase
Cons
- Very early stage with only 15 GitHub stars and limited community adoption
- Requires manual setup and integration with existing MCP infrastructure
- No documented production deployments or case studies
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Provides structured task decomposition from high-level ideas
- Includes dependency tracking and real-time progress visualization
- Open source with a lightweight TypeScript codebase
Cons
- Very early stage with only 15 GitHub stars and limited community adoption
- Requires manual setup and integration with existing MCP infrastructure
- No documented production deployments or case studies
Pairs with
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