TestMyVibes

Submit multiple AI tests as a parallel batch

submit_test_batch

Queue up to 20 AI tests at once and run them in parallel instead of one-after-another. Each test in the batch costs 1.15× its base credits (the parallel premium). Returns the shared batchId and a per-test breakdown so you can poll each jobId individually. Use this when you have an independent set of tests to run (e.g. signup + login + dashboard + settings + delete across one customer site) and want them done in minutes rather than queued through a serial worker. AI runner only — human-runner batching ships separately.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tests`	Yes	Array of 2-20 test specs. Each item has the same shape as submit_test's inputs (AI runner). Tests run concurrently up to a worker concurrency limit of 3.
`projectLabel`	No	Audit label naming which of your projects submitted this batch (e.g. 'shiftsee-regression-suite').

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	No	Tool result payload (JSON object)

TDQS

A4.7/5.0

Behavior5/5

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

Description adds cost detail ('1.15× its base credits'), output structure ('batchId and a per-test breakdown'), and concurrency limit (from input schema note). Annotations are minimal; description provides significant behavioral context beyond annotations.

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?

4 sentences, front-loaded with action and key constraints. Each sentence adds value: definition, cost, return value and polling, usage guidance and exclusion. No waste.

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 the presence of an output schema (referenced), detailed input schema, and behavioral details (parallel execution, concurrency, cost, exclusions), the description is complete for an agent to select and invoke the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema for the two parameters; it only mentions batch size and cost, which are not parameter-specific. No additional semantics provided.

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 the verb 'submit' and resource 'AI tests as a parallel batch', and distinguishes from serial submission with 'instead of one-after-another'. It also notes the batch size limit of 20. This differentiates it from the sibling tool 'submit_test'.

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?

Explicitly says 'Use this when you have an independent set of tests to run... and want them done in minutes rather than queued through a serial worker.' Also specifies 'AI runner only — human-runner batching ships separately,' giving clear when-to-use and when-not-to-use guidance.

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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TDQS

A3.9/5.0

Disambiguation4/5

The tools cover a wide range of functionalities, but each has a clearly distinct purpose. For example, submit_test, submit_test_batch, submit_combo, and submit_interaction_scene are all different types of submissions with unique parameters. However, the sheer number of tools (43) might cause some initial confusion, but descriptors resolve ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_projects, create_project, get_test_results). The only exception is 'whoami', which is a common idiom and does not break the pattern. Overall, naming is highly predictable.

Tool Count3/5

43 tools is on the high side for a single server. The domain is broad (testing, worker marketplace, credits, cards, feedback, video), so the count is justifiable. However, it borders on being overwhelming, and some tools could be consolidated (e.g., multiple submit_* variants).

Completeness3/5

The tool surface covers core workflows like project creation, test submission, result retrieval, worker management, and credit operations. However, there are gaps: no update or delete for projects, no delete for worker offerings, and no user-facing combo editing (though combos are predefined). These are minor but noticeable.

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