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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so safety is covered. The description adds genuine value by disclosing the computed nature of the payload ('numbers you can trust without counting raw rows') and that it emits quality flags such as speeders and duplicate participant ids, giving the agent confidence about data provenance.

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?

A single front-loaded sentence whose clauses each carry payload information, followed by a one-line directive. It is dense and slightly list-heavy but nothing is wasted and the actionable instruction sits at the end for emphasis.

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?

With no output schema and only partial parameter coverage, the description compensates by fully enumerating the return payload, which is exactly what an agent needs to decide whether to call it. Only the studyId parameter's semantics are left implicit.

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 67% and the description says nothing about studyId, workspace, or panelRunId. The schema itself documents workspace and panelRunId well; studyId is self-evident. Nothing is added beyond the schema, so the baseline 3 applies.

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?

States a specific verb+resource ('a study's computed results' / the Results tab) and enumerates the exact content categories returned: fielding funnel, per-question aggregates, quality flags. An agent can tell this apart from sibling read/report tools like prism_studies_report or prism_studies_cut by the quantitative-aggregate framing.

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?

Gives explicit when-to-use ('ALWAYS use this for quantitative questions about a study') plus an anti-pattern ('never tally answers yourself'), which is real routing guidance. It stops short of naming a sibling alternative for narrative/qualitative questions, so it is clear context without full exclusions.

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