Token-optimized multi-agent orchestration MCP server that owns session state, compacts context between agent hops, routes work to smaller models when safe, and reports estimated token savings.
An MCP server that acts as a context-aware code retrieval broker for AI coding agents, enforcing backpressure and token budgeting to prevent context window overflow.
An MCP server that reduces token usage by lazily loading skills and tools only when needed, and routing repetitive subtasks to ML backends instead of the LLM.
MCP server that enables agents to dynamically switch between multiple AI models (OpenAI, Anthropic, Google, etc.) with unified protocol-driven configuration and capability discovery.
MCP server that pools free tiers of multiple LLM providers behind an OpenAI-compatible endpoint, enabling model routing, failover, and usage tracking through tools like tokenmax, capacity, and benchmark.