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retell_create_batch_test

Run batch automated tests to validate Retell LLM and Conversation Flow response engines directly.

Instructions

Run a batch of automated test cases against a Retell LLM or Conversation Flow response engine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
response_engineYesResponse engine to test. Must be { type: 'retell-llm', llm_id: '...' } or { type: 'conversation-flow', conversation_flow_id: '...' }.
reserved_concurrencyNoReserve a portion of org concurrency for the batch test.
test_case_definition_idsYesArray of test case definition IDs to run.
Behavior2/5

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

With no annotations, the description must disclose side effects, resource usage, and outcome, but it only states the action. It does not mention that this creates a batch run, potentially consumes concurrency, whether it is asynchronous, or how results are retrieved. The reserved_concurrency parameter hints at resource impact, but the description itself omits this.

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?

The description is a single clear sentence with no redundant words. It immediately states the action and the subject, making it highly concise and front-loaded.

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?

The tool has no output schema and no annotations, so the description must explain what happens after invocation, but it does not. It is unclear whether the batch test runs synchronously, how results are accessed, or whether concurrency reservation is required. This is a significant gap for a resource-intensive operation.

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?

The schema provides 100% coverage with descriptive text for all three parameters, including allowed structures for response_engine. The description adds no additional parameter detail, but the schema is sufficient, so baseline 3 applies.

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 uses a specific verb phrase ('Run a batch of automated test cases') and clearly specifies the target resources ('Retell LLM or Conversation Flow response engine'). No sibling tool fulfills this testing role, so it is well-distinguished.

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

Usage Guidelines4/5

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

The description clearly implies when to use it: when you need to run automated tests against an LLM or Conversation Flow. It does not explicitly state exclusions or alternative tools, but no competing testing tool exists in the sibling list, so the context is clear.

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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