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batch_download_results

Download and parse results from completed Gemini AI batch jobs. Retrieves JSONL files from the API, saves them locally, and returns structured data arrays for analysis.

Instructions

DOWNLOAD BATCH RESULTS - Download and parse results from completed batch job. WORKFLOW: 1) Checks job status (must be SUCCEEDED), 2) Downloads result file from Gemini API, 3) Parses JSONL results, 4) Saves to local file, 5) Returns parsed results array. RETURNS: Array of results with original keys, responses, and metadata. Also saves to file in outputLocation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batchNameYesBatch job name/ID from batch_create
outputLocationNoDirectory to save results file (defaults to current working directory)
Behavior4/5

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

With no annotations provided, the description carries the full burden and does so well. It discloses key behavioral traits: the multi-step workflow (status check, download, parse, save), prerequisites (job must be SUCCEEDED), side effects (saves to local file), and return format (array with keys, responses, metadata). It lacks details on error handling or rate limits, but covers essential operations.

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?

The description is appropriately sized and front-loaded with the core purpose. The workflow breakdown is useful but slightly verbose; every sentence earns its place by clarifying steps and returns, though it could be more streamlined without losing clarity.

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 no annotations and no output schema, the description does a good job covering complexity: it explains the workflow, prerequisites, return values, and side effects. However, it lacks details on error cases (e.g., if job fails) or file naming conventions, leaving minor gaps for a tool with significant behavioral steps.

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 the schema already documents both parameters fully. The description adds minimal value beyond the schema: it mentions outputLocation defaults to current working directory (implied in schema) and ties batchName to batch_create, but does not provide additional syntax, format, or constraints. Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific action (download and parse results from completed batch job) and distinguishes it from siblings like batch_get_status (which only checks status) or batch_create (which creates jobs). It specifies the resource (batch job results) and the multi-step workflow, making the purpose unambiguous.

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 clear context for when to use this tool: after a batch job has completed with SUCCEEDED status. It implicitly suggests alternatives like batch_get_status for checking status without downloading, but does not explicitly name when-not scenarios or compare to all siblings like batch_cancel or batch_delete.

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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