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retry_failed_datapoints

Re-queue failed survey datapoints to retry them, reserving credit again for each. Use only when failures are worth recovering.

Instructions

Re-queue failed datapoints on a survey.

Each retried datapoint reserves credit again, the same way the original submission did — only call this when the failures are worth recovering.

Args: job_id: The job ID returned by create_survey. datapoint_indices: Specific failed indices to retry. Omit to retry every failed datapoint in the survey.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
datapoint_indicesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Without annotations, the description carries the full burden. It discloses that retrying reserves credit again, which is a notable side effect. However, it does not mention other behavioral traits such as idempotency, rate limits, authorization requirements, or failure/success responses.

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 two concise paragraphs with clear separation. The main action is front-loaded, and the Args section is structured logically. Every sentence adds value without redundancy.

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

Completeness4/5

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

Given the tool's simplicity and the presence of an output schema (not shown), the description covers key aspects: purpose, cost implication, condition to use, and parameter details. It could be improved by mentioning idempotency or limits, but it is largely complete for a retry operation.

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?

With 0% schema coverage, the description adds significant meaning: job_id is clarified as 'the job ID returned by create_survey,' and datapoint_indices is explained with the default behavior ('Omit to retry every failed datapoint'). This fully compensates for the lack of schema descriptions.

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 're-queue' and explicitly states the resource 'failed datapoints on a survey.' It clearly distinguishes this tool from sibling tools like cancel_survey or check_survey by focusing on retrying specific datapoints.

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 provides explicit guidance with 'only call this when the failures are worth recovering,' implying a cost-benefit condition. It also explains the default behavior when datapoint_indices is omitted. However, it does not explicitly state when not to use the tool or mention alternatives like checking survey status first.

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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