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building-agent-tools

为AI代理创建有效工具的指南。在构建MCP工具、代理API或任何代理将使用的工具界面时使用。重点关注令牌效率、有意义的上下文以及正确的命名空间。

person作者: jakexiaohubgithub

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