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Mallikarjun-Roddannavar

ai-testcase-generator-mcp

generate_tests_excel

Generate comprehensive API test plans including positive, negative, and boundary value analysis cases. Exports Excel files with structured test cases for efficient testing.

Instructions

Generate a comprehensive API test plan with positive, negative, and boundary value analysis test cases. Output is Excel with columns: Sl no, Test Name, Pre-Condition, Steps, Expected Result

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endpointYesAPI endpoint to test (e.g., https://api.example.com/v1/users)
methodYesHTTP method (GET, POST, etc.)
payloadNoSample payload for the request (if applicable)
extraContextNoAny additional instructions/context for the test plan
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral transparency. It fails to disclose whether authentication is needed, if the endpoint must be live, potential rate limits, or how the payload is used. The word 'comprehensive' is vague, and there is no mention of what happens with large outputs or errors.

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

Conciseness5/5

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

Two sentences: first covers purpose and test types, second describes output format. No superfluous content. Every sentence adds value. Ideal conciseness for a relatively straightforward tool.

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

Completeness3/5

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

Given the moderate complexity (generating test plans) and lack of output schema, the description should explain the output more (e.g., is it a file download?) and mention potential time or size constraints. It partially covers the output with column names but omits behavioral context. Adequate but with gaps.

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

Parameters3/5

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

Input schema coverage is 100% with clear descriptions for all four parameters, so baseline is 3. The tool description does not add any parameter-specific detail beyond what the schema already provides, so it neither improves nor detracts from the schema's clarity.

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 it generates a comprehensive API test plan with positive, negative, and boundary value analysis, and specifies the Excel output columns. It uses a specific verb ('Generate') and resource ('API test plan'), making the purpose unambiguous, especially since there are no sibling tools to differentiate from.

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

Usage Guidelines4/5

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

The description provides clear context about what the tool does and its output, but does not explicitly state when to use it or when not to. Since there are no sibling tools, the lack of exclusion criteria is less critical, but some guidance on prerequisites (e.g., valid endpoint) would improve it.

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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