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wait_until

Poll server-side for observable conditions like window state, URL changes, or element matches. Eliminates screenshot-polling loops when waiting for state changes.

Instructions

Purpose: Server-side poll for an observable condition — eliminates screenshot-polling loops when waiting for state changes. Details: condition selects what to watch: window_appears/window_disappears (target.windowTitle required), focus_changes (optional target.fromHwnd), element_appears/value_changes (target.windowTitle + target.elementName required, UIA; min 500ms interval), ready_state (target.windowTitle; visible + not minimized), terminal_output_contains (target.windowTitle + target.pattern required [+target.regex:true], needs terminal tools loaded), element_matches (target.by + target.pattern required, needs browser tools loaded), url_matches (target.pattern required [+target.regex:true]; matches the active tab's location.href via CDP — use for SPA route changes, redirects, OAuth flows). Returns {ok:true, elapsedMs, observed} on success, or WaitTimeout error with suggest hints. timeoutMs default 5000 (max 60000). Prefer: Use instead of run_macro({sleep:N}) + screenshot loops. Use terminal_output_contains to detect CLI command completion. Use element_matches for browser DOM readiness after navigation. Use url_matches when the URL is the most reliable signal (SPA routing / redirect cascades). Caveats: terminal_output_contains, element_matches, and url_matches require a browser CDP connection (open --remote-debugging-port=9222 first). element_appears/value_changes spawn a UIA process per poll — interval clamped to 500ms minimum. On WaitTimeout, read the suggest[] array in the error for recovery steps. Examples: wait_until({condition:'window_appears', target:{windowTitle:'Save As'}, timeoutMs:10000}) wait_until({condition:'terminal_output_contains', target:{windowTitle:'Terminal', pattern:'$ '}, timeoutMs:30000}) wait_until({condition:'element_matches', target:{by:'text', pattern:'Submit', scope:'#checkout-form'}}) wait_until({condition:'url_matches', target:{pattern:'/dashboard'}, timeoutMs:15000}) wait_until({condition:'url_matches', target:{pattern:'^https://app\.example\.com/orders/[0-9]+$', regex:true}})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conditionYesCondition to wait for. See per-condition target requirements.
targetNoTarget descriptor — fields used depend on condition. Accepts an object literal or a JSON-stringified object.
timeoutMsNoMaximum time to wait (default 5000ms)
intervalMsNoPoll interval (default 200ms — terminal_output_contains uses 500 internally)
includeNoOptional response-shape opt-in. `['envelope']` returns the self-documenting envelope (`_version` / `data` / `as_of` / `confidence`). `['raw']` forces raw shape (overrides DESKTOP_TOUCH_ENVELOPE=1 server default). Default behaviour is raw shape (compat with existing clients).
Behavior5/5

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

With no annotations provided, the description carries full burden and covers all relevant behaviors: caveats about CDP requirements for certain conditions, UIA process spawn per poll with minimum interval, and WaitTimeout error behavior with suggest hints. Default and maximum timeout values are also noted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Purpose, Details, Prefer, Caveats, Examples) and front-loads essential information. Though lengthy, it uses its length effectively to cover all conditions and edge cases.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters (1 required), no output schema, and no annotations, the description exhaustively covers all conditions, caveats, and usage patterns. Examples cover multiple conditions and edge cases (e.g., regex usage). An AI agent has enough information to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds significant value by detailing required fields for each condition, poll interval behavior, and the purpose of the 'include' parameter. Examples further illustrate parameter usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description begins with a clear statement of purpose: 'Server-side poll for an observable condition — eliminates screenshot-polling loops when waiting for state changes.' It lists all supported conditions with specific resource-target pairings, differentiating this tool from screenshot-based alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Prefer:' section explicitly instructs when to use this tool instead of alternatives like run_macro({sleep:N}) + screenshot loops. It provides condition-specific guidance, e.g., using terminal_output_contains for CLI completion and url_matches for SPA route changes.

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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