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suggest_test_cases

Read-only

Generate template-based test case scaffolding from a feature description, providing structured test ideas with steps and priorities. Use as a project-aware starting point to refine and persist with create_test_case.

Instructions

Return template-based test case scaffolding for a feature description: structured ideas with titles, steps and priorities. This is pattern matching, not model output. Use it as a project-aware starting point, refine it yourself, then persist the result with create_test_case.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "projectId": "<projectId>",
      "featureDescription": "<featureDescription>"
    },
    "name": "suggest_test_cases"
  }
}

Input Schema

Name	Required	Description	Default
`testType`	No	Type of test cases to suggest	all
`projectId`	Yes	Project UUID for context (uses project settings for status/priority values)
`maxSuggestions`	No	Maximum number of test case suggestions
`featureDescription`	Yes	Description of the feature to generate test cases for. Be specific about functionality, user flows, and edge cases.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.5.0Aug 23, 2026

TDQS

A4.3/5.0

Behavior4/5

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

The description reveals important behavioral characteristics not present in the annotations: this is pattern matching, not model output, and the results are scaffolding that require human/AI refinement before persistence. The readOnlyHint already signals safety, but the description adds the expectation that results are provisional and not final artifacts. This meaningfully helps the agent decide how to use the output.

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?

The description is a compact two-sentence definition that front-loads the tool's purpose and then adds just enough behavioral and next-step context. Every clause serves a clear function: output expectation, pattern-matching caveat, and handoff to its sibling. There is no padding or reiteration of schema fields.

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

Completeness5/5

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

Given the fully documented input schema, read-only annotation, and simple output shape stated in the description, the tool is fully scoped. The description even covers what to do with the returned scaffolding, which is essential for a suggestion-only tool. Nothing critical is missing for an agent to correctly invoke it and handle the output.

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?

The input schema already documents all four parameters with 100% coverage, including defaults and constraints. The description adds minimal extra parameter semantics, mostly reinforcing that featureDescription is the source input and projectId makes suggestions project-aware. That is helpful but only tangentially, so the baseline 3 is appropriate.

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 identifies the tool as returning template-based test case scaffolding for a feature description, and specifies the output format (titles, steps, priorities). It explicitly contrasts itself with model output and create_test_case, which helps an agent understand its distinct role among siblings. The verb 'Return' plus the resource 'test case scaffolding' makes the core purpose unambiguous.

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 gives explicit usage context: use this tool to generate project-aware starting suggestions, refine them yourself, then persist them with create_test_case. It implicitly communicates that create_test_case is the follow-on tool for actual creation, but it does not enumerate situations where this tool should be avoided or other alternatives preferred. This is strong but not fully exhaustive guidance.

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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