Advanced Tool Use: Dynamic Tool Binding

Breaking through the 128 function limit with deferred loading

Overview

Most LLM APIs limit you to 128 functions per request. With deferred tool loading, you can scale to unlimited tools by loading them dynamically on-demand.

This implements the patterns described in Anthropic's Advanced Tool Use blog post.

The Problem

# You have 500 database tools
tools = [
    postgres_query, postgres_insert, postgres_update, postgres_delete,
    mongo_find, mongo_insert, mongo_aggregate,
    # ... 493 more tools ...
]

# ❌ ERROR: Most APIs limit to 128 tools
response = client.messages.create(
    model="claude-3-5-sonnet",
    tools=tools  # Too many!
)

The Solution: Dynamic Tool Binding

1. Mark Tools as Deferred

from chuk_tool_processor.registry import register_tool
from chuk_tool_processor.models.validated_tool import ValidatedTool

# Core tools: Always loaded (< 128)
@register_tool(namespace="core")
class CalculatorTool(ValidatedTool):
    pass

# Deferred tools: Loaded on-demand
@register_tool(
    namespace="postgres",
    defer_loading=True,  # 🔑 Key feature!
    search_keywords=["database", "sql", "query", "postgres"],
    tags={"database", "sql"}
)
class PostgresQueryTool(ValidatedTool):
    pass

2. Use Tool Search

The ToolSearchTool is automatically registered in the system namespace:

from chuk_tool_processor.registry import get_default_registry

registry = await get_default_registry()

# Search for tools matching "postgres query"
matches = await registry.search_deferred_tools(
    query="postgres query",
    tags=["database"],
    limit=5
)

# Load matched tools
for tool_meta in matches:
    await registry.load_deferred_tool(tool_meta.name, tool_meta.namespace)

3. Bind Only Active Tools to API

# Get only currently loaded tools
registry = await get_default_registry()
active_tools = await registry.get_active_tools()

# Convert to API format
tool_schemas = []
for tool_info in active_tools:
    tool_class = await registry.get_tool(tool_info.name, tool_info.namespace)
    tool_schemas.append(tool_class.to_anthropic())

# Call API with dynamic tool list
response = client.messages.create(
    model="claude-3-5-sonnet",
    tools=tool_schemas,  # Only loaded tools!
    messages=messages
)

Complete Workflow

┌─────────────────────────────────────────────────────────┐
│ Tool Registry                                           │
│  • 5 core tools (always loaded)                         │
│  • 495 deferred tools (loaded on demand)                │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│ API Call #1: Initial Request                            │
│ Tools: [tool_search, calculator, web_search]            │
│ Count: 3 tools (well under 128 limit!)                  │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│ Claude: "I need PostgreSQL tools"                       │
│ Action: tool_search(query="postgres query")             │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│ Tool Search Returns:                                    │
│  • postgres_query                                       │
│  • postgres_insert                                      │
│  • postgres_transaction                                 │
│ Status: ✅ Tools now loaded                             │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│ API Call #2: With New Tools                             │
│ Tools: [tool_search, calculator, web_search,            │
│         postgres_query, postgres_insert,                │
│         postgres_transaction]                           │
│ Count: 6 tools                                          │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│ Claude: Uses postgres_query(sql="SELECT...")            │
│ Status: ✅ Success!                                      │
└─────────────────────────────────────────────────────────┘

API Reference

Decorator Parameters

@register_tool(
    name: str | None = None,
    namespace: str = "default",
    defer_loading: bool = False,  # Enable deferred loading
    search_keywords: list[str] | None = None,  # Keywords for search
    allowed_callers: list[str] | None = None,  # ['claude', 'programmatic']
    **metadata
)

Registry Methods

# Search deferred tools
await registry.search_deferred_tools(
    query: str,
    tags: list[str] | None = None,
    limit: int = 5
) -> list[ToolMetadata]

# Load a deferred tool
await registry.load_deferred_tool(
    name: str,
    namespace: str = "default"
) -> Any

# Get active (loaded) tools
await registry.get_active_tools(
    namespace: str | None = None
) -> list[ToolInfo]

# Get deferred (not loaded) tools
await registry.get_deferred_tools(
    namespace: str | None = None
) -> list[ToolInfo]

Real-World Examples

Example 1: Database Tool Library

# 500 database tools across 4 databases
@register_tool(namespace="postgres", defer_loading=True, search_keywords=["postgres", "sql", "query"])
class PostgresQueryTool(ValidatedTool):
    pass

# 199 more postgres tools...

@register_tool(namespace="mongodb", defer_loading=True, search_keywords=["mongo", "nosql"])
class MongoFindTool(ValidatedTool):
    pass

# 149 more mongo tools...

# User: "Query my PostgreSQL users table"
# → tool_search finds postgres tools
# → Loads only 3-5 postgres tools
# → Total tools sent to API: < 10

Example 2: Data Processing Pipeline

# Hundreds of specialized data processing tools
@register_tool(namespace="data", defer_loading=True, search_keywords=["csv", "parse"])
class CSVParserTool(ValidatedTool):
    pass

@register_tool(namespace="data", defer_loading=True, search_keywords=["json", "validate"])
class JSONValidatorTool(ValidatedTool):
    pass

@register_tool(namespace="ml", defer_loading=True, search_keywords=["predict", "model"])
class MLPredictTool(ValidatedTool):
    pass

# User workflow loads tools progressively:
# 1. CSV parsing task → loads CSV tools
# 2. JSON validation task → loads JSON tools
# 3. ML prediction task → loads ML tools
# Each step adds only needed tools

Benefits

✅ Unlimited Tools

Before: Limited to 128 tools
After: Thousands of tools, loaded on-demand

✅ Reduced Token Usage

Before: 128 tool schemas in every request
After: 5-10 tool schemas, only what's needed

✅ Faster Response Times

Smaller tool lists = faster API calls
Less parsing overhead for Claude

✅ Better Organization

Namespace-based organization
Clear separation between core and specialized tools
Searchable tool metadata

Migration Guide

Step 1: Identify Core vs Specialized Tools

# Core tools (use frequently, < 10 tools)
CORE_TOOLS = [
    "tool_search",      # Required for discovery
    "calculator",
    "web_search",
    "file_read",
]

# Specialized tools (use occasionally, mark as deferred)
SPECIALIZED_TOOLS = [
    "postgres_*",       # 200 tools
    "mongodb_*",        # 150 tools
    "ml_*",            # 100 tools
    # etc...
]

Step 2: Mark Tools as Deferred

# Before
@register_tool(namespace="postgres")
class PostgresQueryTool(ValidatedTool):
    pass

# After
@register_tool(
    namespace="postgres",
    defer_loading=True,  # ← Add this
    search_keywords=["postgres", "sql", "query"],  # ← And this
)
class PostgresQueryTool(ValidatedTool):
    pass

Step 3: Update API Integration

async def get_tools_for_api():
    """Get tools to send to LLM API."""
    registry = await get_default_registry()

    # Only get active (loaded) tools
    active_tools = await registry.get_active_tools()

    # Convert to API format
    return [
        await get_tool_schema(tool.name, tool.namespace)
        for tool in active_tools
    ]

Step 4: Handle Tool Search Calls

# When Claude calls tool_search, it auto-loads tools
# Just process the response and make a new API call with updated tools

if tool_call.name == "tool_search":
    # Tool search automatically loaded new tools
    # Get updated tool list for next API call
    updated_tools = await get_tools_for_api()

    # Continue conversation with expanded tool set
    response = client.messages.create(
        model="claude-3-5-sonnet",
        tools=updated_tools,  # Now includes newly loaded tools
        messages=messages
    )

Best Practices

1. Choose Good Search Keywords

# ❌ Bad: Too generic
search_keywords=["tool", "data"]

# ✅ Good: Specific and discoverable
search_keywords=["postgres", "sql", "query", "database", "select"]

2. Use Descriptive Namespaces

# ❌ Bad: Everything in default namespace
@register_tool(namespace="default", defer_loading=True)

# ✅ Good: Organized by domain
@register_tool(namespace="postgres", defer_loading=True)
@register_tool(namespace="mongodb", defer_loading=True)
@register_tool(namespace="ml", defer_loading=True)

3. Keep Core Tools Small

# ✅ Aim for < 10 core tools
CORE_TOOLS = 5-10 tools

# Everything else should be deferred
DEFERRED_TOOLS = Unlimited!

4. Add Rich Descriptions

@register_tool(
    namespace="postgres",
    defer_loading=True,
    search_keywords=["postgres", "query", "sql", "select"],
)
class PostgresQueryTool(ValidatedTool):
    """
    Execute PostgreSQL SELECT queries with advanced filtering.

    Supports:
    - Complex WHERE clauses
    - JOINs across tables
    - Aggregations (COUNT, SUM, AVG)
    - LIMIT and OFFSET for pagination
    """
    pass

Performance Considerations

Tool Search is Fast

Keyword matching is O(n) where n = deferred tools
Typically < 1ms for 1000 tools

Lazy Loading is Instant

Tools are imported on first use
Cached after loading
Zero overhead for subsequent uses

Token Savings

85% reduction in tool schema tokens (from Anthropic's blog)
More context available for actual conversation

Troubleshooting

Issue: Tools not found by search

Solution: Improve search keywords

# Add more specific keywords
@register_tool(
    search_keywords=[
        "postgres", "postgresql",  # Database name variants
        "query", "select", "sql",  # Operation types
        "database", "db", "rdbms"  # General terms
    ]
)

Issue: Too many tools being loaded

Solution: Refine search queries

# Instead of generic search
await registry.search_deferred_tools("database")  # Loads too many

# Use specific search
await registry.search_deferred_tools("postgres query")  # Loads only what's needed

Issue: Import errors when loading deferred tools

Solution: Ensure import_path is correct

# The decorator auto-generates import_path
# For manual override:
@register_tool(
    defer_loading=True,
    metadata={"import_path": "my_package.tools.MyTool"}
)

Navigation Menu

Uh oh!

Advanced Tool Use: Dynamic Tool Binding

Overview

The Problem

The Solution: Dynamic Tool Binding

1. Mark Tools as Deferred

2. Use Tool Search

3. Bind Only Active Tools to API

Complete Workflow

API Reference

Decorator Parameters

Registry Methods

Real-World Examples

Example 1: Database Tool Library

Example 2: Data Processing Pipeline

Benefits

✅ Unlimited Tools

✅ Reduced Token Usage

✅ Faster Response Times

✅ Better Organization

Migration Guide

Step 1: Identify Core vs Specialized Tools

Step 2: Mark Tools as Deferred

Step 3: Update API Integration

Step 4: Handle Tool Search Calls

Best Practices

1. Choose Good Search Keywords

2. Use Descriptive Namespaces

3. Keep Core Tools Small

4. Add Rich Descriptions

Performance Considerations

Tool Search is Fast

Lazy Loading is Instant

Token Savings

Troubleshooting

Issue: Tools not found by search

Issue: Too many tools being loaded

Issue: Import errors when loading deferred tools

See Also

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Advanced Tool Use: Dynamic Tool Binding

Overview

The Problem

The Solution: Dynamic Tool Binding

1. Mark Tools as Deferred

2. Use Tool Search

3. Bind Only Active Tools to API

Complete Workflow

API Reference

Decorator Parameters

Registry Methods

Real-World Examples

Example 1: Database Tool Library

Example 2: Data Processing Pipeline

Benefits

✅ Unlimited Tools

✅ Reduced Token Usage

✅ Faster Response Times

✅ Better Organization

Migration Guide

Step 1: Identify Core vs Specialized Tools

Step 2: Mark Tools as Deferred

Step 3: Update API Integration

Step 4: Handle Tool Search Calls

Best Practices

1. Choose Good Search Keywords

2. Use Descriptive Namespaces

3. Keep Core Tools Small

4. Add Rich Descriptions

Performance Considerations

Tool Search is Fast

Lazy Loading is Instant

Token Savings

Troubleshooting

Issue: Tools not found by search

Issue: Too many tools being loaded

Issue: Import errors when loading deferred tools

See Also