What is the primary benefit of using the Building Agent Tools skill?

It helps developers create tool interfaces that are optimized for AI consumption, leading to fewer errors and lower token costs through consolidation and semantic context.

How does this skill address token efficiency?

It provides patterns for pagination, filtering, and smart response truncation to ensure agents only process the most relevant data.

How should I handle tool namespacing?

You should prefix tools with service names (e.g., asana_search) and group them by domain to help agents distinguish functionality easily.

Why is tool consolidation emphasized?

Consolidating tools reduces the number of round-trips an agent needs to make, which improves speed and reliability compared to granular CRUD-style operations.

Does this skill support the Model Context Protocol (MCP)?

Yes, it includes specific patterns for MCP tool registration, schema definition, and structured response formats.

Building Agent Tools

Name: Building Agent Tools
Author: Byunk

byByunk

Desarrollo de API

Standardizes the creation of efficient, high-performance tools and MCP interfaces for AI agents.

Acerca de

This skill provides a comprehensive framework for designing and implementing tools that AI agents can consume effectively, specifically focusing on the Model Context Protocol (MCP). It guides developers through optimizing tool consolidation, namespacing, and token efficiency to ensure agents receive high-signal information without unnecessary overhead. By following these established patterns, developers can build more reliable agentic workflows that minimize errors and maximize performance through clear documentation and semantic response formatting.

Características Principales

Guidelines for tool consolidation to reduce agent API calls
Semantic response formatting to provide agents with meaningful context
0 GitHub stars
Token efficiency patterns including pagination and smart truncation
Best practices for descriptive schemas and JSON-based tool registration
Namespacing strategies for organized and discoverable toolsets

Casos de Uso

Refactoring existing internal APIs to be more AI-native and efficient
Designing Model Context Protocol (MCP) servers for custom agent integrations
Optimizing large data responses for agent-based data analysis workflows

Acerca de

Características Principales

Guidelines for tool consolidation to reduce agent API calls
Semantic response formatting to provide agents with meaningful context
0 GitHub stars
Token efficiency patterns including pagination and smart truncation
Best practices for descriptive schemas and JSON-based tool registration
Namespacing strategies for organized and discoverable toolsets

Casos de Uso

Refactoring existing internal APIs to be more AI-native and efficient
Designing Model Context Protocol (MCP) servers for custom agent integrations
Optimizing large data responses for agent-based data analysis workflows