QMetry: Link Requirements to Testcase

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

The annotations include readOnlyHint=false, destructiveHint=false, and idempotentHint=false, which already indicate a non-read, non-destructive, non-idempotent operation. The description adds context by stating it links requirements (implying modification) and provides output description ('JSON object with success status and linkage details'). It does not contradict annotations, but it does not disclose side effects like whether existing links are replaced or appended, or if there are permissions required. Annotations carry the basic mutation info, so a 3 is appropriate; no contradiction.

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 comprehensive and well-structured with sections for parameters, output, use cases, examples, and hints. It is longer than necessary, but each section earns its place: the hints are highly actionable, and the example is valuable. It is front-loaded with the core purpose, and the structure aids scanning. There is minor redundancy (parameter descriptions echoed from schema), but it's acceptable for usability. Not as concise as the TDQS 4.3 example, hence 4.

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's moderate complexity (3 parameters, no output schema, no nested objects), the description is complete. It includes all necessary information: purpose, parameter semantics, output description, use cases, and critical hints for resolving IDs, which are non-obvious and require external API calls. The annotations provide safety hints, so the description doesn't need to repeat them. This description fully equips the agent to use the tool correctly, surpassing the minimum viable bar.

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?

Schema coverage is 100%, each parameter has a description in the schema. However, the tool description enhances parameter understanding significantly by providing examples (e.g., 'VT-TC-26', '5448515', '5009939,5009937,4970699') and detailed hints on how to obtain each parameter (e.g., 'To get the tcID, call the Testcase/Fetch List for Bulk Operation API and use data[<index>].entityKey'). This goes beyond the schema's basic descriptions, adding practical guidance. Baseline is 3 due to full schema coverage, but the extra detail justifies a 4.

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 the tool's purpose: 'Link one or more requirements to a test case by entityKey and version IDs.' It specifies the action (linking), the resource (requirements to test case), and the identifiers used. It is distinct from sibling tools like qmetry_link_test_cases_to_test_suite, which links test cases to suites. The title and description are consistent, and the toolset label ('Requirements') helps contextualize it.

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 provides explicit guidance on when to use this tool: 'Use Cases: 1. Link requirements to a test case for traceability 2. Bulk link multiple requirements to a single test case 3. Automate requirement coverage mapping.' It also mentions alternatives implicitly by naming the toolset and providing hints to resolve IDs via other APIs. However, it does not explicitly state when NOT to use it, but the use cases and hints are sufficient for differentiation.

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