agentsclimarketplace

Building agent tools

Skill aiskillstore/marketplace/skills/byunk/building-agent-tools

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npx -y skills add aiskillstore/marketplace --skill building-agent-tools

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Guide for creating effective tools for AI agents. Use when building MCP tools, agent APIs, or any tool interface that agents will consume. Focuses on token efficiency, meaningful context, and proper namespacing.

SKILL.md

2.8 KB, 525 tokens by cl100k_base, as published. Nobody here has run it

Building Tools for AI Agents

Workflow

  1. Define Purpose

    • Identify what agents need to accomplish with this tool
    • Determine if existing tools can be consolidated
    • Plan the tool's interface for agent consumption
  2. Design Interface

    • Choose descriptive, namespaced tool names
    • Define parameters with clear types and descriptions
    • Design output format for maximum signal, minimum tokens
  3. Implement

    • Build with token efficiency in mind
    • Add pagination, filtering, sensible defaults
    • Return semantic identifiers, not raw IDs
  4. Validate

    • Test with real agent workflows
    • Check token consumption patterns
    • Verify error messages guide agents toward solutions

Design Principles

Tool Consolidation

  • More tools don't lead to better outcomes
  • Combine related operations into single tools
  • Example: schedule_event that checks availability AND creates event
  • Avoid simple CRUD-style tools that require multiple calls

Namespacing

  • Prefix related tools with service name: asana_projects_search, asana_users_search
  • Group by domain to help agents distinguish functionality
  • Use consistent naming patterns across tool families

Meaningful Context

  • Return high-signal information, not raw data dumps
  • Resolve cryptic UUIDs to human-readable identifiers
  • Include response_format parameter (concise/detailed) for flexibility
  • Surface relevant metadata agents need for next steps

Token Efficiency

  • Implement pagination with sensible defaults
  • Add filtering parameters to reduce unnecessary data
  • Truncate large responses intelligently
  • Prefer structured output over verbose prose

Tool Descriptions

  • Invest heavily in clear, explicit descriptions
  • Describe what the tool does, when to use it, and what it returns
  • Include parameter constraints and valid values
  • Small description improvements yield large performance gains

Anti-Patterns

  • Creating many granular tools instead of consolidated operations
  • Returning raw IDs that agents can't interpret
  • Omitting pagination on potentially large result sets
  • Vague tool descriptions that leave agents guessing
  • Error messages that don't help agents recover
  • Requiring agents to make multiple calls for common workflows

MCP-Specific Patterns

Tool Registration

  • Use descriptive name and description in tool schema
  • Define inputSchema with JSON Schema for parameters
  • Mark required vs optional parameters explicitly

Response Format

  • Return structured JSON for predictable parsing
  • Include success/error indicators
  • Provide actionable error messages

Gives 0 of the 12 instructions most context ai engineering skills give in 525 tokens

Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-06

  • dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
  • dispatch final reviewer after all tasksin 37 of 1193, across 11 files
  • provide full task text to the subagentin 31 of 1193, across 10 files
  • review spec compliance before code qualityin 27 of 1193, across 10 files
  • make the hook script executablein 26 of 1193, across 8 files
  • re-snapshot after navigation or DOM changesin 25 of 1193, across 17 files
  • answer subagent questions before proceedingin 22 of 1193, across 7 files
  • mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
  • merge hook into existing settingsin 21 of 1193, across 3 files
  • read files before editing themin 21 of 1193, across 9 files
  • ask if installation is global or projectin 20 of 1193, across 2 files
  • copy the hook script to target locationin 20 of 1193, across 2 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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