docs/TOOLS_SUMMARY.md

🎯 Tool Calling Integration - Quick Summary

✅ What Was Implemented

1. Tool Executor (tool_executor.py)

• ToolCall dataclass - Represents single tool call with results
• ToolExecutionBatch - Batch of tool calls with statistics
• ToolExecutor - Main executor class
  • Executes tool calls in batch
  • Tracks tokens (input + output)
  • Logs execution times
  • Formats results for LLM

2. LLM Client Updates (llm_client.py)

• Added tool_calls field to LLMResponse
• Support for tools parameter in generate()
• Extracts function calls from Gemini response
• Added tool_tokens tracking to LLMStats
• New method: _convert_tool_schema() for Gemini format

3. AI Manager Integration (ai_manager.py)

• Imports AgentTools and ToolExecutor
• Updates agent tools with game state each turn
• Tool calling loop in _send_to_llm():
  1. Send prompt with tools
  2. Execute any tool calls
  3. Send results back to LLM
  4. Repeat until final answer (max 5 iterations)
• Adds tool tokens to statistics

4. Logging System (ai_logger.py)

• New method: log_tool_execution(batch)
• Logs to llm_communication.log with details
• Saves to tool_executions.json with full data
• Shows:
  • Tool name and parameters
  • Execution time per call
  • Token counts (input/output)
  • Success/failure status
  • Result previews

5. Testing (test_tools_integration.py)

• 4 comprehensive tests:
  • Basic tool operations
  • Multiple tool calls in batch
  • Schema generation
  • Execution history and stats
• Sample outputs and verification

6. Documentation (TOOLS_INTEGRATION.md)

• Complete guide to tool system
• Architecture diagrams
• Usage examples
• Logging format reference
• Troubleshooting guide

---

🔄 How It Works

Game Turn
   ↓
AI Manager updates AgentTools with game state
   ↓
Send prompt to LLM (with tool schemas)
   ↓
LLM responds with tool_calls?
   ↓ YES
Execute tools via ToolExecutor
   ↓
Log execution (time, tokens, results)
   ↓
Send results back to LLM
   ↓
LLM provides final answer

---

📊 What You See in Logs

llm_communication.log

[12:34:56] [TOOL_REQUEST] 🔧 LLM requested 2 tool(s) (iteration 1)
[12:34:56] [TOOL]   ✅ inspect_node({"node_id": 14})
[12:34:56] [TOOL]      Time: 12.3ms | Tokens: 5 in + 45 out = 50 total
[12:34:56] [TOOL]   ✅ find_best_nodes({"min_pips": 10})
[12:34:56] [TOOL]      Time: 32.9ms | Tokens: 10 in + 82 out = 92 total
[12:34:56] [TOOL]   Total: 2/2 successful | 142 tokens | 45.2ms

tool_executions.json

Complete JSON record with:

• Timestamp per batch
• All tool calls with full parameters
• Complete results
• Token breakdown
• Execution times

Token Statistics

{
  "total_tokens": 15432,
  "tool_tokens": 1250,    # NEW: from tools
  "llm_tokens": 14182     # NEW: from LLM only
}

---

🎮 Benefits

For AI Agent

• ✅ No hallucinations - Gets real data from tools
• ✅ Multiple queries - Can ask about several nodes at once
• ✅ Strategic insight - Path analysis, best locations, etc.

For Developers

• ✅ Full visibility - See exactly what tools were called
• ✅ Token tracking - Know the cost of each tool
• ✅ Easy debugging - Detailed logs with timing
• ✅ Performance monitoring - Execution statistics

For System

• ✅ Prevents infinite loops - Max 5 tool iterations
• ✅ Graceful errors - Failed tools don't crash the system
• ✅ Scalable - Easy to add new tools

---

🧪 Testing

Run:

python examples/ai_testing/test_tools_integration.py

Expected: All 4 tests pass with detailed output

---

📁 Files Created/Modified

New Files

• pycatan/ai/tool_executor.py - Tool execution engine
• examples/ai_testing/test_tools_integration.py - Test suite
• docs/TOOLS_INTEGRATION.md - Complete documentation

Modified Files

• pycatan/ai/llm_client.py - Function calling support
• pycatan/ai/ai_manager.py - Tool integration
• pycatan/ai/ai_logger.py - Tool logging

Existing (Used)

• pycatan/ai/agent_tools.py - The 3 tools (already existed)

---

🚀 Next Steps

The system is ready to use! The LLM can now:

1. ✅ Call inspect_node() for specific nodes
2. ✅ Call find_best_nodes() to search board
3. ✅ Call analyze_path_potential() for road planning
4. ✅ Make multiple calls in one turn
5. ✅ All executions are logged and tracked

Just run the game normally - tools are automatically available!

---

💡 Example Usage

The AI will automatically use tools when needed:

AI thinking: "I need to know about node 14..."
  → Calls: inspect_node(14)
  → Gets: {"node_id": 14, "total_pips": 12, "resources": {...}}
  → Decides: "Great location, I'll build there!"

All of this is logged automatically in:

• llm_communication.log (real-time)
• tool_executions.json (detailed batch records)

---

✅ Summary

What you asked for:

• ✓ Support for multiple tool calls (e.g., query two nodes)
• ✓ Clear logging of tool usage
• ✓ Complete token calculation from tools
• ✓ Visible tool loop with parameters and outputs

What you got:

• Complete tool calling system with Gemini integration
• Detailed execution logging with timing and tokens
• Automatic token tracking separate from LLM
• Test suite to verify everything works
• Full documentation

הכל מוכן לשימוש! 🎉