prepare_feature_bootstrap

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

With no annotations, the description carries the full burden. It explicitly states that no LLM call happens server-side, that the tool fetches specs and anchors, and returns data for client-side generation. This clarifies what the tool does and does not do, providing adequate transparency about its behavior and side effects (none).

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 concise and well-structured: a single sentence summarizing the purpose, a clear statement about no server-side LLM, then a bullet list of required and optional parameters. Every sentence adds value without repetition. It is front-loaded with the core purpose.

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?

For a tool with 7 parameters, no output schema, and no annotations, the description provides substantial context: the workflow, parameter meanings, and a recommendation for style_from_suite_id. It explains what the tool returns (data for client to drive generation) but does not specify the output structure. The omission of project_id is a gap, but overall it is quite complete.

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 description coverage is 0%, so the description must compensate. It explains the meaning and allowed values for source_type, source_value, section_name (hierarchy allowed), new_suite_name vs existing_suite_id, and style_from_suite_id (recommendation). However, it omits project_id entirely, which is a parameter in the schema. Thus, it adds value for most parameters but leaves one undocumented.

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 that the tool prepares a feature-import workflow by fetching specs and house-style anchors, returning data for client-driven generation and push. It distinguishes from siblings like prepare_cases_from_confluence by outlining a two-step process (this tool, then add_test_cases_bulk). The verb 'prepare' and resource 'feature bootstrap' are specific and well-defined.

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 explicitly lists required and optional parameters and provides context on when to use each (e.g., new_suite_name vs existing_suite_id, style_from_suite_id recommended for new empty suites). It details the workflow: call this tool, then add_test_cases_bulk. However, it does not explicitly exclude scenarios where direct import tools like prepare_cases_from_confluence would be more appropriate.

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