Finds relevant Model Context Protocol (MCP) tools by intent using semantic search for efficient AI tool discovery.
Tool Compass solves the challenge of managing and interacting with numerous MCP tools by enabling semantic discovery. Instead of loading dozens or hundreds of tool definitions into an LLM's context, which wastes tokens and slows down responses, it allows users and agents to find precisely the tools they need by describing their intent. This approach dramatically reduces token consumption and improves the efficiency of AI agents, offering a streamlined way to navigate complex tool ecosystems through natural language queries.
Key Features
01Semantic Search for tool discovery by natural language intent
02Hot Cache for frequently used tools to optimize performance
03Analytics for tracking usage patterns and tool performance
040 GitHub stars
05Automated Chain Detection to identify common tool workflows
06Progressive Disclosure for controlled tool interaction (compass, describe, execute)
Use Cases
01Automating the setup and execution of multi-tool workflows for complex tasks
02Efficiently discovering specific MCP tools for LLM agents based on a task description
03Reducing token costs and latency in AI applications that interact with many tools
