list_studiesLists the authenticated user's Studies with their Audiences, Minds, status, sharing state, and workspace or shared links. Accepts an optional fuzzy name search. A Study is the persistent workspace that contains its Audiences, questions, multi-question blocks, results, exports, and history.
| Name | Required | Description | Default |
|---|---|---|---|
| searchQuery | No | Search for a Study by name (fuzzy matching supported) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description does not need to restate safety. It adds useful behavioral context: the scope is limited to the authenticated user's Studies, and it specifies which fields are returned and that name search is fuzzy. It does not contradict 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 sentences, front-loaded with the operation and resource, followed by a crisp definition of a Study. No filler or redundancy; every clause adds information.
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?
For a simple read-only list operation with one optional parameter and no output schema, the description covers scope, returned fields, filtering, and the domain concept. Nothing essential for calling the tool correctly is missing.
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 single parameter searchQuery is already fully described in the schema (fuzzy matching by Study name); the description essentially repeats 'optional fuzzy name search'. With 100% schema coverage, no additional parameter semantics are needed.
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 ('Lists'), a clear resource ('the authenticated user's Studies'), and enumerates the included fields (Audiences, Minds, status, sharing state, links). This makes the tool's purpose unambiguous and distinguishes it from resource-specific siblings like list_audiences and list_study_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 when to use the tool: when you need a user's Studies along with their associated data and optional fuzzy name search. However, it gives no explicit guidance about when to prefer list_study_drafts, list_audiences, or other sibling tools, and mentions no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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The tool set has three distinct get_panel_* tools and two draft-saving tools (plan_panel_study, save_study_draft) that could be confused, but the detailed descriptions clarify their specific scopes. Most other tools (list, create, export, ask) target clearly different resources or actions.
All tool names follow a consistent verb_noun scheme in snake_case (list_, get_, create_, ask_, export_, plan_, run_, save_). Verb choice maps predictably to the operation, making the set easy to navigate.
At 18 tools, the set is slightly above the ideal 3-15 range but well-scoped for a comprehensive research-panel platform. Each tool addresses a distinct part of the workflow, from group/panel creation to study planning, execution, and export.
The surface covers the full research lifecycle: create groups/panels, ask questions, monitor status, export artifacts, and plan/run multi-question studies. It lacks update/delete operations for groups and panels, but these are minor gaps that agents can work around for typical research flows.