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.

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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):

{
  "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:

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:

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):

# 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):

# 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):

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:

@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):

# 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):

# 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):

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:

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:

import anthropic

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

2. Use Tool Search Tool:

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:

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:

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:

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:

# 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:

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:

# 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
• Tool Search Tool Specification
• Programmatic Tool Calling Guide
• Tool Use Examples Best Practices

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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