List Study Recipes

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

Annotations already signal readOnly, idempotent, and non-destructive. The description adds what the response includes (effective priors, settings, data hashes) and explicitly states raw datasets are omitted, which is helpful behavioral context beyond the schema. It does not contradict the annotations.

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?

Two short sentences, front-loaded with the verb and scope, followed by a precise inclusion/exclusion statement. No filler or repetition of annotation hints.

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 rich annotations, an output schema, and a single obvious parameter, the description is nearly sufficient for correct invocation. It covers scope, content, and the raw-dataset exclusion. Minor gaps remain: no ordering/pagination note and no explicit relationship to the recipe-draft tools, but these are not required for a basic read-only list.

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 provides only study_id with no description (0% coverage), and the description never explains what study_id refers to or how it shapes the returned revisions. The parameter is simple and self-descriptive, but the description adds no parameter-level meaning and does not compensate for the schema's lack of documentation.

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 'Read all recipe revisions,' a specific verb and resource, and clarifies scope ('all') and content ('exact effective priors, settings and data hashes'). The explicit exclusion of raw datasets prevents confusion with upload/list_uploads tools. It distinguishes from sibling list_recipe_drafts by focusing on revisions rather than drafts.

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 implies the tool is for reading the full set of recipe revisions for a study, and the 'Raw datasets are omitted' sentence gives one boundary, but it never explicitly states when to prefer this over get_recipe_revision or list_recipe_drafts, nor does it name alternatives. Usage must be inferred from the tool name and purpose.

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