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Dispatch autonomous testing agent

wopee_dispatch_agent

Run end-to-end tests by dispatching an autonomous AI agent that executes pre-existing test cases in a real browser, captures screenshots, and reports pass/fail results. Requires generated test cases.

Instructions

Execute a specific test case by dispatching an autonomous AI agent. The agent opens a real browser, navigates the web app, follows the test case steps, captures screenshots at each step, and reports pass/fail with detailed findings. Prerequisite: test cases must exist in the suite — generate them first with wopee_generate_artifact (type USER_STORIES_WITH_TEST_CASES). Do NOT use this to analyze or crawl an app — use wopee_dispatch_analysis for that. Side effects: creates execution records and screenshots on the Wopee.io platform. Rate limit: 10 seconds between dispatches per project; concurrent calls auto-retry with exponential backoff. On success, returns executed test case results. On failure (invalid suite/test case ID), returns an error message. Use wopee_fetch_executed_test_cases afterward to get full results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
suiteUuidYesUUID of the suite to dispatch the agent for
analysisIdentifierYesAnalysis identifier of the suite to dispatch the agent for
testCasesYesChosen test cases to dispatch the agent for
Behavior4/5

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

With no annotations provided, the description carries full burden and discloses side effects ('creates execution records and screenshots'), rate limiting ('10 seconds between dispatches'), and retry behavior ('concurrent calls auto-retry with exponential backoff'). Could more explicitly categorize the write/modification nature but implies it through side effects.

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?

Information-dense with logical flow covering execution mechanism, prerequisites, exclusions, side effects, rate limits, and return handling. Despite length, every sentence provides actionable guidance with no redundant content.

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?

Thoroughly complete for a complex dispatch tool with no output schema. Covers preconditions, error conditions ('invalid suite/test case ID'), side effects, rate limiting, and post-execution workflow integration. Describes return values adequately given the complexity.

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

Parameters4/5

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

Schema has 100% description coverage. The description adds workflow semantics beyond the schema by clarifying relationships between parameters (test cases belong to suites/user stories) and valid input states through the prerequisite section.

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?

Opens with a specific verb+resource ('Execute a specific test case by dispatching an autonomous AI agent') and clearly distinguishes from sibling tool wopee_dispatch_analysis ('Do NOT use this to analyze or crawl an app — use wopee_dispatch_analysis'). Also references prerequisite tool wopee_generate_artifact.

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?

Provides explicit prerequisite workflow ('generate them first with wopee_generate_artifact'), explicit alternative for different use cases ('use wopee_dispatch_analysis for that'), and explicit follow-up action ('Use wopee_fetch_executed_test_cases afterward').

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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