Enables AI agents to offload mechanical, high-token work to local Ollama models through MCP, with role-based model discovery, batch processing, and file-aware inputs.
Enables MCP clients like Claude Code, Codex, and others to offload bounded background tasks (e.g., repository scans, reviews, batch work) to smaller models configured in OMP, while the parent agent retains context and decision-making.
MCP server that enables an agent to spawn sub-agents (a crew) via a local tool, allowing delegation of large tasks to parallel workers with a human approval gate and efficient token usage.
Delegates heavy, repetitive, and verifiable tasks like PDF extraction, code analysis, and log processing to a local LLM to reduce token consumption for frontier AI models, while keeping decision-making with the main AI.
Connects local LLMs to external tools (calculator, knowledge base) via MCP protocol, enabling automatic tool detection and execution to enhance query responses.