Enables AI agents to write and execute Python code in an isolated sandbox that can orchestrate multiple MCP tool calls, reducing context window bloat and improving efficiency for complex workflows.
Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.
A Model Context Protocol implementation that enables LLMs to execute complex, multi-step workflows combining tool usage with cognitive reasoning, providing structured, reusable paths through tasks with advanced control flow.
Converts JSON data and system prompts to and from TOON (Token-Oriented Object Notation) format, reducing token usage by 30-60% when interacting with LLMs while preserving data structure.
A state-based agent orchestration system that allows transitions between different states (IDLE, PLANNING, RESEARCHING, EXECUTING, REVIEWING, ERROR) while maintaining conversation context and providing state-specific prompts.
Converts Markdown files into professional diagrams such as flowcharts, mind maps, and architecture diagrams using Mermaid.js and custom SVG renderers. It enables users to list, render, and export visualizations in multiple formats including SVG, PNG, and PDF.
MCP server that enables AI to control Blender 3D, providing 175 typed tools for objects, materials, animation, compositing, and more via the Model Context Protocol.
drand-mcp-server is a service that provides verifiable random numbers for model-driven processes in AI applications, supporting the acquisition of random numbers by time or round.