get_analytics_summary

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the tool returns an AI-generated text summary (typically 200–400 words) and highlights a critical prerequisite: analytics must be generated first, with steps to verify or generate. This adds meaningful context beyond the basic read operation, though it could also mention potential error behaviors or side effects if analytics are unavailable.

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 well-structured with a clear opening sentence, bullet-pointed alternatives, a highlighted prerequisite, return value, and argument listing. It is slightly longer than strictly necessary but every sentence serves a purpose, and the front-loading of the main use case is effective. The explicit 'Args:' section is somewhat redundant with the schema but helpful.

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 simplicity (single parameter, output schema exists), the description is fully complete for an agent. It covers the return value (short summary text), the prerequisite workflow, when to use it, and how to select deeper sibling tools. There are no significant gaps in information needed for correct invocation.

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 schema has zero description coverage, so the description must compensate. It provides a minimal definition: 'study_id: The unique identifier of the study.' While this is sufficient for a single self-explanatory parameter, it adds little beyond the parameter name itself. It does not include examples, format hints, or how to obtain the ID, which would have raised the score.

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 verb ('Get') and resource ('a short, concise AI-generated text summary of the study's overall findings'). It also distinguishes itself from sibling tools by explicitly listing follow-up tools for deeper analysis, such as get_qualitative_insights and get_quantitative_data. This goes beyond a generic purpose and fully differentiates from alternatives.

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 usage guidance: 'Use this first when a user asks for a quick overview of study results.' It names specific sibling tools for deeper dives and provides a clear prerequisite workflow (check get_analytics_status, then generate_analytics if needed). This is exemplary guidance with alternatives and exclusions.

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