generate_study_report

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

With no annotations provided, the description carries full burden. It discloses that generation takes 3-5 minutes, the call returns immediately, processing runs in the background, and gives exact polling behavior (up to 10 checks) and user messaging. This is exemplary transparency.

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 structured with clear prerequisites, timing, polling instructions, and next steps. Every sentence provides essential operational detail; no redundancy or filler. The length is justified by the complexity of the asynchronous workflow.

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 complexity (async background generation, dependencies, polling), the description covers all necessary context: prerequisites, expected duration, immediate return behavior, polling policy, failure fallback, and handoff to retrieval. An output schema exists, so not explaining return values is acceptable.

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 provides only the parameter name and type (string) with no description (0% coverage). The description fills this gap by stating 'The unique identifier of the study,' adding clarity beyond the schema. While minimal, it properly compensates for the otherwise undocumented parameter.

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 begins with 'Trigger generation of the comprehensive study report,' using a specific verb and resource. It clearly distinguishes from the sibling tool get_study_report (which retrieves the report) and generate_analytics (a prerequisite step).

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 states prerequisites (analytics must be generated first), tells the agent to call get_analytics_status to check, provides conditional instruction to call generate_analytics if missing, and specifies to poll get_analytics_status and then call get_study_report. This is comprehensive when-to-use guidance.

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