Tool Use Inefficiencies

Enhancing Tool Use Capabilities

AI agents often struggle with effectively using tools at their disposal. At VrealSoft, we’ve developed comprehensive approaches to improve tool usage.

Clear Tool Definition

Tip

Well-defined tool specifications significantly improve an agent’s ability to select and use appropriate tools.

# Example of a well-structured tool definition
tools = [
    {
        "name": "search_database",
        "description": "Search the customer database for matching records",
        "parameters": {
            "query": {
                "type": "string",
                "description": "The search term to look for"
            },
            "limit": {
                "type": "integer",
                "description": "Maximum number of results to return",
                "default": 5
            }
        },
        "returns": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "id": "string",
                    "name": "string",
                    "details": "object"
                }
            }
        },
        "examples": [
            {
                "input": {"query": "customer_id:12345", "limit": 1},
                "output": [{"id": "12345", "name": "John Smith", "details": {...}}]
            }
        ]
    }
]

Tool Selection Training

Few-Shot Examples

Providing clear examples of when and how to use each tool

Tool Taxonomies

Organizing tools into categories to aid in selection

Decision Trees

Explicit guidance on which situations warrant specific tools

Parameter Formulation Improvements

Agents often struggle with correctly formatting parameters for tools. Our approaches include:

1. Schema validation to verify parameter formats before execution 2. Repair mechanisms to fix minor formatting issues automatically 3. Interactive correction for guiding the agent to fix parameter problems 4. Progressive disclosure of advanced parameters as needed

Parameter Validation

def validate_and_repair_parameters(tool_call):
    tool_name = tool_call["name"]
    params = tool_call["parameters"]

    # Get tool definition
    tool_def = get_tool_definition(tool_name)

    # Validate required parameters
    for param_name, param_def in tool_def["parameters"].items():
        if param_def.get("required", False) and param_name not in params:
            return {
                "valid": False,
                "error": f"Missing required parameter: {param_name}",
                "suggestion": f"Add the {param_name} parameter, which should be a {param_def['type']}"
            }

    # Type checking and repairs
    for param_name, param_value in params.items():
        if param_name in tool_def["parameters"]:
            param_def = tool_def["parameters"][param_name]

            # Type validation
            if not validate_type(param_value, param_def["type"]):
                # Try to repair
                repaired_value = attempt_repair(param_value, param_def["type"])
                if repaired_value:
                    params[param_name] = repaired_value
                else:
                    return {
                        "valid": False,
                        "error": f"Parameter {param_name} has incorrect type",
                        "suggestion": f"Expected {param_def['type']}, got {type(param_value).__name__}"
                    }

    return {"valid": True, "parameters": params}

Interactive Correction

async def execute_tool_with_correction(agent, tool_call):
    # Validate parameters
    validation = validate_and_repair_parameters(tool_call)

    if validation["valid"]:
        # Execute the tool if parameters are valid
        return await execute_tool(tool_call["name"], validation["parameters"])
    else:
        # Guide the agent to fix the issues
        correction_prompt = f"""
        Your tool call had the following issue: {validation['error']}

        Suggestion: {validation['suggestion']}

        Please try again with fixed parameters.
        """

        # Get corrected tool call from agent
        corrected_call = await agent.generate_response(correction_prompt)

        # Extract and validate the new tool call
        new_tool_call = extract_tool_call(corrected_call)
        return await execute_tool_with_correction(agent, new_tool_call)

Result Interpretation Training

Caution

Even when tools are called correctly, agents often struggle to properly interpret and use the results.

Our approach includes:

• Structured outputs with clear metadata about result meaning
• Interpretation examples showing how to use different result types
• Context retention to help agents maintain awareness of previous results
• Result summarization for large or complex tool outputs

# Example of structured tool result
def format_tool_result(raw_result, tool_name):
    # Create a structured result with metadata
    formatted_result = {
        "tool_name": tool_name,
        "timestamp": datetime.now().isoformat(),
        "status": "success" if raw_result else "no_results",
        "result_type": determine_result_type(raw_result),
        "result_summary": summarize_result(raw_result),
        "result": raw_result,
        "usage_guidance": get_usage_guidance(tool_name, raw_result)
    }

    return formatted_result

Execution Management

Tool Chains

Predefined sequences of tools for common tasks

Reflective Execution

Having agents evaluate the effectiveness of tool use

Iterative Refinement

Progressively improving tool calls based on results

Training and Evaluation System

We’ve developed a comprehensive training and evaluation system for tool use:

Training Approach

tool_use_training = {
    "methods": [
        {
            "name": "demonstration_learning",
            "description": "Providing examples of correct tool usage",
            "examples_per_tool": 5
        },
        {
            "name": "error_correction",
            "description": "Showing common mistakes and their fixes",
            "examples_per_error_type": 3
        },
        {
            "name": "tool_exploration",
            "description": "Guided discovery of tool capabilities",
            "exploration_tasks_per_tool": 2
        }
    ],
    "evaluation": {
        "metrics": [
            "selection_accuracy",  # Using the right tool
            "parameter_correctness",  # Formatting parameters properly
            "result_utilization",  # Using the results effectively
            "efficiency"  # Minimizing unnecessary tool calls
        ],
        "test_scenarios": 100,  # Number of evaluation scenarios
        "human_verification": True  # Human review of complex tool use
    }
}

Performance Metrics

def evaluate_tool_usage(agent, test_cases):
    results = {
        "tool_selection": {
            "correct": 0,
            "incorrect": 0,
            "missed_opportunities": 0
        },
        "parameter_formulation": {
            "correct": 0,
            "fixable_errors": 0,
            "critical_errors": 0
        },
        "result_interpretation": {
            "fully_utilized": 0,
            "partially_utilized": 0,
            "misinterpreted": 0
        },
        "efficiency": {
            "optimal_calls": 0,
            "acceptable_calls": 0,
            "excessive_calls": 0
        }
    }

    for case in test_cases:
        # Run the agent on the test case
        trace = run_agent_with_tracing(agent, case.input)

        # Evaluate the agent's performance
        case_results = evaluate_trace(trace, case.expected)

        # Update overall results
        update_results(results, case_results)

    return calculate_metrics(results)

Continuous Improvement System

We track tool usage patterns and failures to continuously improve our agents:

# Tool usage monitoring system
class ToolUsageMonitor:
    def __init__(self):
        self.usage_stats = {}
        self.failure_patterns = {}
        self.improvement_suggestions = []

    def record_tool_call(self, agent_id, tool_call, result, success):
        # Record basic usage statistics
        tool_name = tool_call["name"]
        if tool_name not in self.usage_stats:
            self.usage_stats[tool_name] = {
                "calls": 0,
                "successes": 0,
                "failures": 0,
                "params": {}
            }

        self.usage_stats[tool_name]["calls"] += 1
        if success:
            self.usage_stats[tool_name]["successes"] += 1
        else:
            self.usage_stats[tool_name]["failures"] += 1
            self.record_failure_pattern(tool_name, tool_call, result)

    def record_failure_pattern(self, tool_name, tool_call, result):
        # Analyze and categorize failure patterns
        failure_type = categorize_failure(tool_call, result)

        if failure_type not in self.failure_patterns:
            self.failure_patterns[failure_type] = {
                "count": 0,
                "examples": []
            }

        self.failure_patterns[failure_type]["count"] += 1
        if len(self.failure_patterns[failure_type]["examples"]) < 5:
            self.failure_patterns[failure_type]["examples"].append({
                "tool_call": tool_call,
                "result": result
            })

        # Generate improvement suggestions
        if self.failure_patterns[failure_type]["count"] >= 10:
            suggestion = generate_improvement_suggestion(failure_type,
                self.failure_patterns[failure_type]["examples"])
            self.improvement_suggestions.append(suggestion)

    def get_insights(self):
        return {
            "usage_stats": self.usage_stats,
            "common_failures": sorted(
                self.failure_patterns.items(),
                key=lambda x: x[1]["count"],
                reverse=True
            ),
            "improvement_suggestions": self.improvement_suggestions
        }

Note

Our tool usage monitoring system has helped us decrease tool usage errors by 78% over six months of deployment.

Future Directions

We continue to explore:

• More sophisticated parameter repair capabilities
• Natural language interfaces for tool parameter specification
• Tool composition for complex tasks
• Multi-modal tool interactions (text, vision, etc.)
• Adaptive tool interfaces based on agent capabilities