analyze_user_story

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

With no annotations provided, the description carries the full burden. It explicitly enumerates the output contents (decomposition, acceptance criteria, scenarios per layer, risk map, quality questions, test data, checklist), making it clear this is a read/analysis operation with no side effects. However, it doesn't specify output format or any constraints, but it is sufficient for a non-mutating tool.

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

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

The description is a single sentence that front-loads the main purpose and then lists the output components. It is efficient with no wasted words, though it is dense and could be slightly restructured for readability. Overall, it earns its length.

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

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

Given the tool has no output schema, the description adequately summarizes the tool's rich output (a framework with multiple sections). It covers the tool's behavior and what to expect, though it could mention the output format or any limitations. Still, it is complete enough for a comprehensive analysis tool.

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

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

The input schema already provides descriptions for all four parameters (story, context, tech_stack, feature_type) with 100% coverage. The tool description does not add additional parameter semantics beyond what the schema states, so it meets the baseline for full coverage without extra value.

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

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

The description clearly states it analyzes a User Story and returns a comprehensive QA analysis framework, listing specific components (decomposition, acceptance criteria, scenarios per layer, risk map, quality questions, test data, checklist). This distinguishes it from sibling tools that focus on specific test artifacts (e.g., create_gherkin_scenarios, generate_performance_plan).

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

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

The description implies use for obtaining a complete QA analysis of a user story, but it does not explicitly state when to use this tool versus alternatives like generate_test_strategy or create_gherkin_scenarios. No exclusions or alternative guidance is provided, so usage context is only implicit.

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