get_participant_sessions_report

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

No annotations are provided, so the description carries the full transparency burden. It discloses key behavioral traits: the requirement to generate analytics first for best results, and the dependency on get_participants to obtain the correct participant_id. The verb 'Get' implies read-only, and the focus on aggregated reporting communicates expected output. However, it doesn't explicitly state that the operation is non-mutating or mention how stale analytics might affect the report, which would have pushed this to a 5.

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 and front-loaded: a one-sentence summary, followed by usage rationale, sibling alternatives, a clear prerequisite callout, and an Args section. Every sentence earns its place; there is no filler or redundant information. The use of a warning emoji and logical paragraph breaks improves scannability.

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, two simple parameters, and the presence of an output schema, the description is remarkably complete. It covers the tool's purpose, usage context, alternatives, prerequisites, and parameter semantics. The mention of analytics generation and participant lookup fills the potential contextual gaps that an agent would need to invoke it successfully.

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 0% description coverage, so the description fully compensates by defining each parameter: 'study_id: The unique identifier of the study' and 'participant_id: The unique identifier of the participant.' It also adds practical guidance by advising to call get_participants first to find the correct participant_id, which is highly useful for an agent invoking the tool.

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 opens with a clear, specific statement: 'Get an aggregated report for all sessions completed by a specific participant.' This uses a specific verb (Get), a clear resource (sessions report), and a defined scope (specific participant). It also distinguishes itself from siblings by explicitly naming get_sessions_report (across ALL participants) and get_session_report (single session), making its unique purpose unmistakable.

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?

It provides explicit when-to-use guidance: 'Use when the user wants to understand one person's overall experience or performance across multiple sessions in the same study.' It additionally states when NOT to use it by directing users to alternative tools for other needs. The prerequisite note (generate_analytics, poll get_analytics_status, call get_participants) gives clear operational context for correct invocation.

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