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adb_farm_run

Uploads an APK and test APK to Firebase Test Lab, then executes instrumented tests on specified devices and API levels.

Instructions

Run tests on Firebase Test Lab. Uploads an APK and test APK, executes instrumented tests across specified device models and API levels. Requires gcloud CLI authenticated with a Firebase project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appApkYesPath to the app APK file
testApkYesPath to the test/instrumentation APK file
devicesNoDevice specs as 'model=DEVICE,version=API' (e.g., 'model=bluejay,version=33'). Omit for default device.
testTargetsNoSpecific test class or method (e.g., 'class com.example.MyTest')
timeoutNoTest timeout (e.g., '300s', '10m')300s
resultsBucketNoGCS bucket for results (omit for default)
Behavior2/5

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

With no annotations, the description bears full burden but only mentions uploading APKs and execution. It does not disclose what happens after invocation (e.g., async job creation, results location, cleanup), nor any side effects or permissions. The gcloud prerequisite is helpful but insufficient for transparent behavior.

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

Conciseness5/5

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

Two sentences, no filler. First sentence clearly conveys purpose, second adds critical prerequisite. Every word earns its place, and the structure is front-loaded with the core action.

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

Completeness2/5

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

Given no output schema, the description should hint at what the tool returns (e.g., test job ID, results link). It omits any mention of results or asynchronous behavior, leaving the agent guessing about post-invocation state. The resultsBucket parameter is defined in schema but not referenced in description to explain where results go.

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 coverage is 100%, so baseline is 3. The description adds value by connecting parameters to the workflow: 'uploads an APK and test APK' maps to appApk and testApk, 'across specified device models and API levels' maps to devices. This goes beyond repeating schema descriptions and contextualizes them.

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 clearly states it runs instrumented tests on Firebase Test Lab, uploading APK and test APK, and executing across specific devices. This distinguishes it from sibling tools like adb_farm_matrix (likely runs matrix tests) and adb_farm_results (retrieves results), providing a specific verb-resource combination.

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

Usage Guidelines3/5

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

The description implies usage for running instrumented tests on Firebase Test Lab among many adb test tools, but lacks explicit guidance on when to use this versus alternatives like adb_ci_run_tests or adb_farm_matrix. It only mentions a prerequisite (gcloud CLI authenticated with Firebase project) without 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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