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create_test

Define test cases for AI agents by specifying queries, expected tools, forbidden tools, and output constraints. Automatically saves to the project's tests directory.

Instructions

Create a new EvalView test case YAML file for an agent. Call this when the user asks to add a test, or when you want to capture expected agent behavior. After creating a test, call run_snapshot to establish the baseline. No YAML knowledge required — just describe the test. IMPORTANT: Automatically detect test_path by looking for a 'tests/evalview/' directory in the current project. If found, use it. Otherwise use 'tests'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesTest name (e.g. 'calculator-division', 'weather-lookup')
queryYesThe input query to send to the agent
descriptionNoHuman-readable description of what this test covers
expected_toolsNoTool names the agent should call (e.g. ['calculator', 'search'])
forbidden_toolsNoTool names the agent must NEVER call. Any violation is an immediate hard-fail (score=0, passed=false) regardless of output quality. Use this for safety contracts — e.g. a read-only agent that must never call edit_file, bash, or write_file. Matching is case-insensitive: 'EditFile' catches 'edit_file'.
expected_output_containsNoStrings that must appear in the agent's output
min_scoreNoMinimum passing score 0-100 (default: 70)
test_pathNoDirectory to save the test file. Auto-detect: use 'tests/evalview/' if it exists in the project, otherwise 'tests'.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description discloses the main effect (creating a YAML file) and the auto-detection behavior for test_path. It does not cover permissions or potential side effects, but for a file creation tool this is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with the purpose first, followed by context and an important note. It is appropriately sized for the complexity, though could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 8 parameters and no output schema, the description provides sufficient context: what the tool does, when to use it, auto-detection logic, and next step. It is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all 8 parameters with descriptions (100% coverage). The description adds value by noting auto-detection for test_path and that users can describe tests naturally. This exceeds the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates a new EvalView test case YAML file for an agent, with specific use cases (user asks to add test, capture expected behavior). It distinguishes from siblings like generate_skill_tests by focusing on a single test creation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to call ('when user asks to add a test, or when you want to capture expected agent behavior') and provides post-creation guidance ('call run_snapshot'). Also reassures that no YAML knowledge is required.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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