Does this work with any MCP server?

Yes, these are architectural patterns designed to be implemented across any Model Context Protocol server or custom toolset used by LLMs.

How does progressive detail save tokens?

It provides full data for highly relevant results but only minimal summaries for lower-confidence matches, focusing your token budget on the most important information.

What are JSON automation flags?

Flags like has_more, truncated, and confidence allow AI agents to understand the state of a response and decide whether to fetch more data or warn the user programmatically.

Why use ID systems for cross-tool references?

Instead of repeating large data payloads between different tool calls, you can use short, unique IDs to reference previous context, potentially saving up to 80% in token usage.

What is the main benefit of using this skill?

It ensures your MCP tools are both human-friendly and machine-parseable, while significantly reducing token consumption through smart data referencing and status flags.

MCP Response Optimization

Name: MCP Response Optimization
Author: standardbeagle

bystandardbeagle

•

API 开发

Optimizes MCP tool responses for human readability and machine-parseable automation using JSON flags and progressive detail.

关于

This skill provides standardized patterns for designing Model Context Protocol (MCP) responses that balance the needs of human users and AI agents. It implements advanced techniques like token-saving ID systems for cross-tool references, automation flags (such as has_more and truncated), and confidence-based progressive detail levels. By applying these patterns, developers can maximize token efficiency and ensure that AI agents can programmatically navigate complex data sets while providing clear, scannable summaries for human oversight.

主要功能

Dual-format response design for both human scanning and machine parsing
Permissive parameter handling that accepts and warns rather than rejecting inputs
1 GitHub stars
Progressive detail levels that adjust output based on relevance or confidence
Token-efficient ID systems to eliminate data repetition between tools
Standardized JSON automation flags for pagination and state tracking

使用场景

Building search tools that return high-detail for top matches and low-detail for others to save tokens
Designing complex MCP workflows where tools reference previous results via IDs
Creating automation-ready tool outputs that AI agents can navigate using pagination flags

关于

主要功能

Dual-format response design for both human scanning and machine parsing
Permissive parameter handling that accepts and warns rather than rejecting inputs
1 GitHub stars
Progressive detail levels that adjust output based on relevance or confidence
Token-efficient ID systems to eliminate data repetition between tools
Standardized JSON automation flags for pagination and state tracking

使用场景

Building search tools that return high-detail for top matches and low-detail for others to save tokens
Designing complex MCP workflows where tools reference previous results via IDs
Creating automation-ready tool outputs that AI agents can navigate using pagination flags