update_utUpdate a draft UT's metadata (any of: title, background, hypotheses, recruitmentCriteria). Only works on PREPARING drafts; cannot change status or edit UTs that are already running or finished.
| Name | Required | Description | Default |
|---|---|---|---|
| utId | Yes | ||
| title | No | ||
| background | No | ||
| hypotheses | No | ||
| recruitmentCriteria | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations: it restricts updates to PREPARING drafts, disallows status changes, and clarifies that only listed metadata fields can be updated. This complements the readOnlyHint=false and idempotentHint=true annotations without contradiction.
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 two sentences with clear front-loading of the action and resource, followed by a concise constraint. Every word earns its place, and it is free of redundancy.
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 tool with 5 parameters and no output schema, the description covers the essential aspects: what it does, which fields can be updated, and the state constraint. It does not detail return values or error behavior, but these are less critical for an update operation. Overall, it provides sufficient context for correct usage.
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 0%, so the description must compensate for missing parameter details. It lists the updatable fields but provides no semantic explanation for each (e.g., what 'background' or 'recruitmentCriteria' means), relying on self-explanatory names. The description adds no meaningful parameter-level guidance beyond the field names.
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 clearly states the action ('Update'), the resource ('a draft UT's metadata'), and explicitly lists the modifiable fields (title, background, hypotheses, recruitmentCriteria). It also distinguishes itself from sibling tools like create_ut and get_ut by focusing on updating metadata only.
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 provides explicit guidance on when the tool is applicable: only for PREPARING drafts, and explicitly states it cannot change status or edit running/finished UTs. This gives clear when-to-use and when-not-to-use context, though it does not name alternative tools.
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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Every tool targets a distinct resource and action. Even get_ut, which returns comprehensive data, doesn't replace the specialized get_session, get_transcript, or get_quantitative_report for detailed views. No two tools have overlapping purposes.
All names follow a verb_noun pattern with underscores (e.g., add_task, get_ut, list_workspaces). The main inconsistency is using 'create' for UTs (create_ut) but 'add' for child entities (add_use_case, add_task), and having a separate update_scenario instead of folding it into update_ut, but the convention is still predictable.
With 18 tools, the set is slightly above the typical 3-15 well-scoped range, but the domain of usability testing requires distinct operations for drafting (use cases, tasks, scenario), reading results (reports, sessions, transcripts), and comparison. Each tool has a clear role, so the count feels reasonable rather than excessive.
The tool surface covers the core lifecycle: create, read, update, and delete for tasks/use cases, plus comprehensive retrieval of results and notes. Minor gaps include no delete_ut (UTs cannot be removed) and no explicit status change or cloning mechanism, but these are likely intentional app-side actions, so the surface is not severely incomplete.