Progressive Discovery

About

The Progressive Discovery skill provides a standardized framework for implementing 'info' and 'help' tools within MCP servers, solving the problem of tool discovery in complex environments. By utilizing a tiered approach—moving from sparse overviews to detailed documentation—it prevents token waste and cognitive overload for AI agents and human users alike. This pattern includes templates for sparse tables, machine-readable JSON outputs, and common workflow guides, ensuring that users can find the exact capability they need without sifting through exhaustive documentation for every single interaction.

Key Features

• Multi-layered discovery patterns ranging from sparse overviews to full tool documentation
• Token-optimized output formats including sparse tables and machine-parseable JSON
• Pre-defined workflow templates to guide users through common multi-tool sequences
• Scalable implementation guides for MCP servers ranging from 5 to 20+ tools
• Permissive parameter handling patterns to mitigate AI model hallucinations
• 1 GitHub stars

Use Cases

• Managing complex MCP servers with high tool counts that overwhelm standard documentation
• Onboarding users to new MCP capabilities through structured discovery and help systems
• Reducing token consumption in AI-driven workflows by providing layered info access