building-agent-tools

Agent Building

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.

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Source SKILL.md: https://github.com/aiskillstore/marketplace/blob/HEAD/skills/byunk/building-agent-tools/SKILL.md

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Building Tools for AI Agents

Workflow

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
Design Interface
- Choose descriptive, namespaced tool names
- Define parameters with clear types and descriptions
- Design output format for maximum signal, minimum tokens
Implement
- Build with token efficiency in mind
- Add pagination, filtering, sensible defaults
- Return semantic identifiers, not raw IDs
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