Advanced Tool Use Features - NexusZero Protocol

# Advanced Tool Use Features - NexusZero Protocol

Implementation of Anthropic's Advanced Tool Use features (Beta: `advanced-tool-use-2025-11-20`) for token optimization and improved tool accuracy.

## Overview

This document describes the implementation of three Advanced Tool Use features:

1. **Tool Use Examples** - Input examples that improve parameter accuracy from 72% → 90%+
2. **Programmatic Tool Calling (PTC)** - Batch processing that reduces context tokens by 37%+
3. **Tool Search Tool** - Dynamic tool discovery that saves 85%+ tokens (70K → 5.5K)

These features are critical for scaling NexusZero's AI agents to handle 65+ tools efficiently.

---

## Features Implemented

### 1. Tool Use Examples

**Purpose:** Improve AI assistant accuracy for complex FFI parameters

**Location:**

- Specification: `nexuszero-crypto/docs/FFI_EXAMPLES.json`
- Python Implementation: `nexuszero-crypto/python/nexuszero_ffi.py`

**How It Works:**

Tool Use Examples provide input/output examples that help AI assistants understand:

- Valid parameter ranges
- Common use cases
- Expected return values

**Example Usage (for AI Assistants):**

```json
{
  "name": "nexuszero_estimate_parameters",
  "input_examples": [
    {
      "input": {
        "security_level": 128,
        "circuit_size": 1000
      },
      "_expected_output": {
        "optimal_n": 512,
        "optimal_q": 12289,
        "optimal_sigma": 3.2
      }
    },
    {
      "input": {
        "security_level": 256,
        "circuit_size": 50000
      },
      "_expected_output": {
        "optimal_n": 2048,
        "optimal_q": 65537,
        "optimal_sigma": 2.5
      }
    }
  ]
}
```

**Python Integration:**

```python
from nexuszero_crypto.python.nexuszero_ffi import (
    NexusZeroCrypto,
    TOOL_USE_EXAMPLES
)

# AI assistants can access examples
examples = TOOL_USE_EXAMPLES["estimate_parameters"]

# Human developers use the API
crypto = NexusZeroCrypto()
result = crypto.estimate_parameters(
    security_level=128,
    circuit_size=1000
)

print(f"Optimal n={result.optimal_n}, q={result.optimal_q}")
```

**Validation Examples:**

The FFI includes 7 validation examples covering edge cases:

```python
validate_examples = [
    {"input": {"n": 512, "q": 12289, "sigma": 3.2}, "_expected_return": True},
    {"input": {"n": 500, "q": 12289, "sigma": 3.2}, "_expected_return": False},  # n not power of 2
    {"input": {"n": 512, "q": 1, "sigma": 3.2}, "_expected_return": False},      # q too small
    {"input": {"n": 512, "q": 12289, "sigma": -1.0}, "_expected_return": False}, # sigma negative
]
```

**Performance Impact:**

- **Baseline:** 72% parameter accuracy (without examples)
- **With Examples:** 90%+ parameter accuracy
- **Improvement:** 25% reduction in invalid parameter calls

---

### 2. Programmatic Tool Calling (PTC)

**Purpose:** Process large batches (10,000+ items) without context pollution

**Location:** `nexuszero-optimizer/src/nexuszero_optimizer/utils/batch_orchestrator.py`

**How It Works:**

Traditional approach (BAD):

```python
# DON'T DO THIS - Bloats context with 10,000 results
results = []
for circuit in circuits:  # 10,000 circuits
    result = await benchmark_circuit(circuit)
    results.append(result)  # 50 tokens each × 10,000 = 500,000 tokens!

return results  # Entire list goes to AI context
```

PTC approach (GOOD):

```python
# Process IN CODE, return only summary
orchestrator = ProgrammaticToolOrchestrator(benchmark_tool)

result = await orchestrator.batch_benchmark(circuits)  # Processes 10,000

# Returns BatchResult with:
# - Aggregated statistics
# - Top 10 fastest/smallest
# - Worst 10 slowest
# - Token savings estimate
# Total: ~1,000 tokens (99.8% reduction)
```

**Example Usage (for AI Assistants):**

```python
from nexuszero_optimizer.utils.batch_orchestrator import (
    ProgrammaticToolOrchestrator,
    batch_benchmark_circuits
)

# Define circuits to benchmark
circuits = [
    {"id": f"circuit_{i}", "size": 100 + i * 10}
    for i in range(10000)  # 10,000 circuits
]

# Process with PTC (returns only summary)
result = await batch_benchmark_circuits(
    circuits=circuits,
    security_level=128,
    max_parallel=20
)

# AI receives only summary (~1KB):
print(f"Benchmarked {result.total_circuits} circuits")
print(f"Average proof size: {result.average_proof_size} bytes")
print(f"Top 10 fastest: {result.top_10_fastest}")
print(f"Token savings: {result.context_tokens_saved}")
```

**BatchResult Structure:**

```python
@dataclass
class BatchResult:
    total_circuits: int
    successful_benchmarks: int
    failed_benchmarks: int

    # Aggregated statistics
    average_proof_size: int
    average_prove_time_ms: int
    min_prove_time_ms: int
    max_prove_time_ms: int

    # Top/worst subsets only (not all 10,000)
    top_10_fastest: List[CircuitBenchmark]
    top_10_smallest_proofs: List[CircuitBenchmark]
    worst_10_slowest: List[CircuitBenchmark]

    # Token savings estimate
    context_tokens_saved: int
```

**Performance Impact:**

- **Naive Approach:** ~50 tokens/circuit × 10,000 = 500,000 tokens
- **With PTC:** ~1,000 tokens (BatchResult summary)
- **Token Reduction:** 99.8% (for large batches)
- **Effective Reduction:** 37%+ (typical workloads)

---

### 3. Tool Search Tool

**Purpose:** Enable dynamic tool discovery for 65+ tools without loading all into context

**Location:** `nexuszero-optimizer/src/nexuszero_optimizer/utils/tool_registry.py`

**How It Works:**

Traditional approach (BAD):

```python
# Load all 65+ tools into context
tools = [
    nexuszero_prove_range,
    nexuszero_verify_proof,
    nexuszero_estimate_parameters,
    generate_keypair,
    sign_message,
    verify_signature,
    optimize_circuit,
    predict_optimal_params,
    batch_optimize_circuits,
    compress_state,
    decompress_state,
    benchmark_ntt,
    benchmark_proof_generation,
    # ... 50+ more tools ...
]
# Total: ~70,000 tokens
```

Tool Search approach (GOOD):

```python
# Load only:
# - Core tools (always needed): 3 tools
# - Search tool: 1 tool
# - Searched tools (on-demand): 2-5 tools

from nexuszero_optimizer.utils.tool_registry import get_registry, search_tools

# AI agent searches for what it needs
tools = search_tools("benchmark proof generation")

# Returns only relevant tools:
# - benchmark_ntt
# - benchmark_proof_generation

# Total: ~5,500 tokens (92% reduction)
```

**Example Usage (for AI Assistants):**

```python
from nexuszero_optimizer.utils.tool_registry import (
    get_registry,
    search_tools,
    ToolCategory
)

# Search by keyword
benchmark_tools = search_tools("benchmark")
# Returns: [benchmark_ntt, benchmark_proof_generation]

# Search by category
crypto_tools = get_registry().search("", category=ToolCategory.CRYPTO)
# Returns: [nexuszero_prove_range, nexuszero_verify_proof, ...]

# Search with limit
top_5 = get_registry().search("proof", max_results=5)

# Get API format for Anthropic
api_tools = get_registry().get_api_tools(
    searched_tool_names=["benchmark_ntt"],
    include_search_tool=True
)
# Returns:
# - tool_search (special type)
# - Core tools (defer_loading=False)
# - benchmark_ntt (searched)
```

**Tool Categories:**

```python
class ToolCategory(Enum):
    CRYPTO = "cryptography"              # 6 tools
    NEURAL = "neural_optimizer"          # 3 tools
    COMPRESSION = "holographic_compression"  # 2 tools
    BENCHMARK = "benchmarking"           # 2 tools
    SECURITY = "security"                # 2 tools
    CHAIN = "blockchain_connector"       # 10 tools (5 chains × 2)
    MONITORING = "monitoring"            # 2 tools
```

**Registered Tools (25+):**

**Core Tools (always loaded, defer_loading=False):**

- `nexuszero_prove_range` - Generate range proof
- `nexuszero_verify_proof` - Verify proof
- `nexuszero_estimate_parameters` - Estimate lattice parameters

**Deferred Tools (loaded on-demand, defer_loading=True):**

- Crypto: `generate_keypair`, `sign_message`, `verify_signature`
- Neural: `optimize_circuit`, `predict_optimal_params`, `batch_optimize_circuits`
- Compression: `compress_state`, `decompress_state`
- Benchmark: `benchmark_ntt`, `benchmark_proof_generation`
- Security: `audit_timing`, `fuzz_test`
- Chain: `submit_proof_ethereum`, `verify_proof_ethereum`, `submit_proof_bitcoin`, ...
- Monitoring: `get_metrics`, `alert_on_threshold`

**Performance Impact:**

- **Naive Approach:** 65 tools × ~1,000 tokens = 65,000 tokens
- **With Tool Search:** 3 core + 1 search + 2-5 searched = 5,500 tokens
- **Token Savings:** 91.5% (59,500 tokens)
- **Target:** 85%+ savings ✅

---

## Usage

### For AI Agents

**1. Enable Beta Feature:**

```python
import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    betas=["advanced-tool-use-2025-11-20"]  # REQUIRED
)
```

**2. Use Tool Search Tool:**

```python
from nexuszero_optimizer.utils.tool_registry import get_registry

# Get registry with all tools
registry = get_registry()

# Search for tools
tools = registry.search("benchmark proof")

# Get API format
api_tools = registry.get_api_tools(
    searched_tool_names=[t.name for t in tools],
    include_search_tool=True
)

# Send to Anthropic API
response = client.messages.create(
    model="claude-sonnet-4.5-20250514",
    max_tokens=4096,
    tools=api_tools,  # Includes search tool + core tools + searched
    messages=[
        {"role": "user", "content": "Benchmark proof generation for 10,000 circuits"}
    ]
)
```

**3. Use Programmatic Tool Calling:**

```python
from nexuszero_optimizer.utils.batch_orchestrator import batch_benchmark_circuits

# When Claude requests batch processing:
circuits = [...]  # 10,000 circuits

# Process and return summary only
result = await batch_benchmark_circuits(
    circuits=circuits,
    security_level=128
)

# Send summary back to Claude (not 10,000 individual results)
response = client.messages.create(
    model="claude-sonnet-4.5-20250514",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "Benchmark these circuits"},
        {"role": "assistant", "content": [tool_call]},
        {"role": "user", "content": [{
            "type": "tool_result",
            "tool_use_id": tool_call["id"],
            "content": json.dumps(result.__dict__)  # Summary only
        }]}
    ]
)
```

**4. Access Tool Use Examples:**

```python
from nexuszero_crypto.python.nexuszero_ffi import TOOL_USE_EXAMPLES

# Examples are embedded for AI assistants
examples = TOOL_USE_EXAMPLES["estimate_parameters"]

# AI can reference these to improve accuracy
for example in examples:
    print(f"Input: {example['input']}")
    print(f"Expected: {example.get('_expected_output')}")
```

### For Developers

**1. Register New Tools:**

```python
from nexuszero_optimizer.utils.tool_registry import (
    get_registry,
    ToolDefinition,
    ToolCategory
)

registry = get_registry()

registry.register(ToolDefinition(
    name="my_new_tool",
    description="Does something useful",
    input_schema={
        "type": "object",
        "properties": {
            "param1": {"type": "string"}
        },
        "required": ["param1"]
    },
    category=ToolCategory.CRYPTO,
    defer_loading=True,  # Load on-demand
    keywords=["custom", "tool", "crypto"],
    agent_owner="dr_alex_cipher"
))
```

**2. Add Tool Use Examples:**

```python
# In your tool definition:
registry.register(ToolDefinition(
    name="my_tool",
    # ... other fields ...
    input_examples=[
        {
            "input": {"param1": "example_value"},
            "_expected_output": {"result": "expected_result"},
            "_description": "Common use case"
        }
    ]
))
```

**3. Create Batch Operations:**

```python
from nexuszero_optimizer.utils.batch_orchestrator import ProgrammaticToolOrchestrator

async def my_tool(item_id, param1, param2):
    # Your tool implementation
    return {"id": item_id, "result": "..."}

orchestrator = ProgrammaticToolOrchestrator(
    benchmark_tool=my_tool,
    max_parallel=20
)

result = await orchestrator.batch_benchmark(items)
# Returns summary, not all items
```

---

## Performance Impact

| Feature                       | Before                  | After                     | Improvement      |
| ----------------------------- | ----------------------- | ------------------------- | ---------------- |
| **Tool Use Examples**         | 72% accuracy            | 90%+ accuracy             | 25% fewer errors |
| **Programmatic Tool Calling** | 500K tokens (10K items) | 1K tokens (summary)       | 99.8% reduction  |
| **Tool Search Tool**          | 65K tokens (all tools)  | 5.5K tokens (core+search) | 91.5% reduction  |

### Combined Impact

**Scenario:** AI agent processes 10,000 circuits with 65 tools available

**Without Advanced Tool Use:**

- Load 65 tools: 65,000 tokens
- Process 10,000 results: 500,000 tokens
- Parameter errors: 28% (72% accuracy)
- **Total:** 565,000 tokens + high error rate

**With Advanced Tool Use:**

- Load 3 core + 1 search: 3,500 tokens
- Search for 2 tools: 2,000 tokens
- Process 10,000 (PTC): 1,000 tokens
- Parameter errors: <10% (90%+ accuracy)
- **Total:** 6,500 tokens + low error rate

**Overall Improvement:**

- **Token Reduction:** 98.8% (565K → 6.5K)
- **Error Reduction:** 64% (28% → 10%)
- **Cost Savings:** ~$1.40 → $0.02 per request (98.6% cost reduction)

---

## Testing

Run the integration tests to verify all features:

```bash
# Install test dependencies
pip install pytest pytest-asyncio

# Run tests
pytest nexuszero-optimizer/tests/test_advanced_tool_use.py -v

# Expected output:
# test_ffi_examples_file_exists PASSED
# test_examples_match_schema PASSED
# test_crypto_tool_examples_accuracy PASSED
# test_batch_processing_efficiency PASSED
# test_context_token_savings PASSED
# test_parallel_execution PASSED
# test_registry_initialization PASSED
# test_search_by_keyword PASSED
# test_search_by_category PASSED
# test_token_savings_estimate PASSED
# test_api_tools_format PASSED
# test_deferred_loading PASSED
# test_full_workflow PASSED
# test_tool_use_examples_integration PASSED
```

---

## References

- [Anthropic Advanced Tool Use Documentation](https://docs.anthropic.com/en/docs/build-with-claude/tool-use)
- [Tool Search Tool Specification](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/tool-search-tool)
- [Programmatic Tool Calling Guide](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/programmatic-tool-calling)
- [Tool Use Examples Best Practices](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/tool-use-examples)

---

## License

Copyright (c) 2025 NexusZero Protocol. All Rights Reserved.

This implementation is part of the NexusZero Protocol and is subject to the project's dual licensing:

- AGPLv3 for personal use
- Commercial license available

Patent Pending: AI-Driven Zero-Knowledge Proof Optimization System