Skynet-MCP

Skynet‑MCP tackles the challenge of orchestrating complex, multi‑step tasks across a distributed set of AI agents. In many production workflows, a single LLM is insufficient to handle everything from data extraction and analysis to code generation and reporting. Skynet‑MCP creates a hierarchical network where each agent can spawn child agents, inherit the full toolset of its parent, and delegate subtasks to specialized models or services. This recursive structure enables parallel execution, deeper reasoning chains, and the ability to scale a single prompt into a coordinated team effort.

At its core, Skynet‑MCP is both an MCP server and an MCP client. As a server it exposes a rich set of tools—most notably for launching agent tasks and for polling asynchronous results. As a client it can connect to other MCP servers, automatically discovering and importing their tools. This bidirectional capability means that a Skynet‑MCP instance can act as a hub, aggregating tools from OpenAI, Anthropic, or any custom service, and then hand those tools to its child agents. Developers can therefore build a multi‑model ecosystem where each model contributes its unique strengths without needing to manage separate endpoints manually.

Key capabilities include:

• Dual‑mode operation: Seamlessly switch between acting as a provider of tools and a consumer that consumes tools from others.
• LLM integration: Plug in any LLM provider (OpenAI, Anthropic) via a simple configuration object.
• Tool discovery: Automatically pull in tools from connected MCP servers, keeping the agent’s toolbox up‑to‑date.
• Hierarchical agent management: Spawn, supervise, and terminate child agents on demand, enabling task decomposition.
• Transport flexibility: Communicate over STDIO or Server‑Sent Events, accommodating both local and remote deployments.
• Asynchronous execution: Use to run long‑running tasks in the background and retrieve results later, improving throughput.

Real‑world use cases abound. A data‑science team could launch a parent agent that ingests raw logs, then spawn child agents to clean data, run statistical models, and generate visual reports—all coordinated by a single high‑level prompt. A software company could use Skynet‑MCP to automate feature development: a parent agent receives a product requirement, spawns coding agents that call GPT‑4 for code generation, and testing agents that invoke unit tests, finally aggregating the results. Because each child inherits its parent’s toolset, developers can focus on defining task logic rather than wiring disparate services.

Integrating Skynet‑MCP into existing AI workflows is straightforward: expose the server to your tooling layer, reference its tool in prompts, and let the system handle agent creation and communication. Its FastMCP foundation guarantees low latency and robust error handling, while the configurable transport options make it suitable for both local development and cloud‑scale deployments. In short, Skynet‑MCP provides a scalable, composable framework that turns isolated LLM calls into coordinated, multi‑agent operations—an essential step toward truly autonomous AI systems.