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.
MCP server for HamQTH.com — callsign lookup, DX cluster spots, Reverse Beacon Network, DXCC resolution, and more through any MCP-compatible AI assistant.
Enables integration with Kagi search engine services including web search, content summarization from URLs, and AI assistant conversations. Uses session tokens to access Kagi's search API, summarizer, and AI models directly within MCP-compatible applications.
Provides access to Agno framework documentation for AI agents, enabling search and retrieval of SDK references, API endpoints, code examples, and integration guides through MCP-compatible tools.
A local MCP server providing free network access capabilities including HTTP requests, web search, webpage content extraction, and optional screenshots, designed for integration with Claude Desktop or Claude Code.