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Anthropic Tool Use Patterns & Advanced Integration

Anthropic Tool Use Patterns & Advanced Integration

Research Date: December 2025 Purpose: Document advanced tool use patterns and optimization strategies Sources: Anthropic documentation, engineering blogs, CODITECT implementation

Executive Summary​

Advanced tool use patterns enable 75-85% token reduction via Tool Search Tool API while maintaining full functionality. Key optimization strategies include programmatic tool calling, selective tool registration, and intelligent tool discovery.

1. Tool Search Tool API​

Overview​

The Tool Search Tool (TST) enables semantic search across available tools, reducing token costs by 75-85%.

Benefits​

  • Token Reduction: 75-85% reduction via lazy loading
  • Scalability: Unlimited tools without context bloat
  • Discovery: Semantic search finds relevant tools
  • Performance: Sub-second tool discovery

Implementation Pattern​

# Register tools with metadata
tools = [
    {
        "name": "git_sync",
        "description": "Synchronize git submodules bottom-up",
        "keywords": ["git", "sync", "submodule", "automation"]
    }
]

# Tool Search Tool searches semantically
relevant_tools = tool_search("synchronize repositories")
# Returns: [git_sync, ...]

2. Programmatic Tool Calling​

Patterns​

  1. Parallel Calls - Independent operations
  2. Sequential Calls - Dependent operations
  3. Conditional Calls - Based on results
  4. Batch Calls - Multiple similar operations

Best Practices​

  • Call multiple independent tools in parallel
  • Use sequential calls only when necessary
  • Validate tool availability before calling
  • Handle errors gracefully with fallbacks

3. CODITECT-Specific Optimizations​

Component Activation Integration​

Tool use integrated with component activation system:

# Check activation before tool call
if component_activated("git-sync"):
    result = call_tool("git_sync", params)
else:
    request_activation("git-sync")

Progressive Tool Discovery​

  1. Level 1: Core tools (always available)
  2. Level 2: Common tools (activated on-demand)
  3. Level 3: Specialized tools (manual activation)

Token Efficiency​

  • Metadata-only registration (50-100 tokens/tool)
  • Full schemas loaded on-demand (500-1000 tokens/tool)
  • 75-85% reduction vs. full registration

4. Tool Use Workflow​

Discovery Phase​

  1. User describes task
  2. Tool Search Tool finds relevant tools
  3. Claude reviews tool schemas
  4. Claude selects appropriate tools

Execution Phase​

  1. Validate tool availability
  2. Prepare tool parameters
  3. Execute tool calls (parallel where possible)
  4. Process results
  5. Handle errors/retries

Optimization Phase​

  1. Cache frequently used tools
  2. Batch similar operations
  3. Minimize redundant calls
  4. Log tool usage for analytics

5. Best Practices​

Tool Registration​

  • Use descriptive names and descriptions
  • Include rich metadata (keywords, examples)
  • Document expected parameters clearly
  • Provide usage examples

Tool Calling​

  • Validate inputs before calling
  • Handle errors gracefully
  • Use parallel calls when possible
  • Log all tool invocations

Performance​

  • Lazy load tool schemas
  • Cache tool metadata
  • Batch similar operations
  • Monitor token usage

6. Source References​

  1. Anthropic Tool Use Documentation
  2. Advanced Tool Use Integration
  3. CODITECT tool use implementation patterns

Last Updated: December 2025 Status: Production implementation