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

Annotations declare destructiveHint=true and readOnlyHint=false. Description adds significant value: it runs against inference credit (cost), stores output on the study, and may return needs_confirmation (a possible pause requiring approvalId). This is useful behavioral context beyond annotations. However, it doesn't state whether it overwrites existing narrative or how destructive the operation is.

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?

Well-structured single paragraph, front-loaded with the core action; each sentence adds information (what, source, storage, cost, possible confirmation).

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 no output schema and only 3 params, the description adequately covers the action, source data, storage location, cost implication, and potential confirmation return. Could note approvalId relationship explicitly, but otherwise 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 coverage is only 33%, so description must compensate for undocumented parameters. It doesn't mention studyId, workspace, or approvalId semantics. The mention of needs_confirmation implies approvalId is relevant, but doesn't explain the parameter. Baseline 3 is generous given the gap.

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?

Specific verb+resource: 'An AI research analyst writes the report's narrative layer' from the study's computed record, naming the exact components (executive summary, key findings, recommendations). Clearly distinguishes from siblings like prism_studies_analyze (computes the record) and prism_studies_results (returns raw results).

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?

Implies usage by describing the source ('study's computed record (funnel, per-question aggregates, themes, quotes)') and output placement, but does not state when to call this vs prism_studies_analyze or prism_studies_results, nor prerequisites.

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