Scripts
AuraScore 83/100

Multi-Turn Autonomous Tool-Calling Evaluation Scenario Script

Design an automated evaluation script that stress-tests an agent's tool-selection accuracy, parameter extraction, and boundary handling.

Use this template when setting up automated regression and safety test suites for LLM tool-calling agents. It produces executable test scripts with mock environments, ambiguous prompts, and strict assertion logic.

Template

Role: Senior Agent Safety and Evaluation Engineer specializing in autonomous reasoning benchmarks and tool-calling validation suites.

Context

  • Operational Domain: {{agent_domain}}
  • Target Tool Catalog: {{allowed_toolset}}
  • Ambiguous Intent Scenarios: {{ambiguous_intents}}
  • Adversarial Test Vectors: {{adversarial_edge_cases}}
  • Benchmark Strictness Criteria: {{strictness_threshold}}
  • Execution Harness: {{test_runner_framework}}

Task

Generate an automated test suite script using {{test_runner_framework}} that evaluates an autonomous agent's tool-calling accuracy, hallucination resistance, and recovery behavior across {{agent_domain}} scenarios.

Method

  1. Define mock implementations of all tools in {{allowed_toolset}} that simulate successes, rate limits, and malformed responses.
  2. Construct synthetic multi-turn conversation fixtures incorporating {{ambiguous_intents}} to assess clarification triggers.
  3. Formulate adversarial injection test cases from {{adversarial_edge_cases}} to evaluate agent tool authorization gates.
  4. Implement deterministic assertion functions that evaluate tool choice correctness, argument validity, and invocation order.
  5. Write negative assertion tests ensuring zero invocations of non-existent or hallucinated functions.
  6. Compute aggregate reliability scores based on {{strictness_threshold}} across parameter precision and sequence efficiency.
  7. Structure parameterized test fixtures to enable parallel execution against varied model backend endpoints.

Constraints

  • Test script MUST be written as a valid, executable {{test_runner_framework}} test file with zero placeholder mock data.
  • Tests MUST assert that the agent asks clarifying questions when faced with {{ambiguous_intents}} instead of guessing tool parameters.
  • Assertions must evaluate both raw JSON schema compliance and semantic validity of tool payloads.
  • Hallucinated function calls MUST result in an immediate hard failure of the test scenario.

Output format

Provide the response structured as follows:

  1. Test Suite Configuration & Fixtures (Mock tools and environment setups)
  2. Parameterized Test Cases Script (Full test methods covering nominal, ambiguous, and adversarial runs)
  3. Benchmark Evaluation Metric Hook (Custom metric calculators outputting summary pass/fail metrics)

Self-review

  • Check that every edge case in {{adversarial_edge_cases}} has a corresponding explicit test method.
  • Confirm that mock tool fixtures accurately simulate boundary conditions without external network dependencies.
  • Ensure assertions strictly enforce the criteria stated in {{strictness_threshold}}.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

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