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Get Batch Job Status

replicate_batch_status
Read-onlyIdempotent

Check the status of an async batch prediction job. Get overall status (running, completed, partial) and optional per-item results to monitor progress.

Instructions

Poll the status of an async batch job started with replicate_batch_start.

Args:

  • job_id (string): Job ID returned by replicate_batch_start.

  • include_results (boolean, default true): Include full PredictionResult per item. Set false for a counts-only summary while the job is still running.

Returns structuredContent: { job_id, overall_status, total, succeeded, failed, running, pending, created_at, expires_at, items: [{ index, model, status, prediction_id, result?, error?, started_at, completed_at }] }

overall_status: "running" — predictions still in progress "completed" — all items succeeded "partial" — all done, at least one failed

Tip: Poll every 10–30 seconds until overall_status is "completed" or "partial".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesJob ID returned by replicate_batch_start.
include_resultsNoInclude full PredictionResult per completed item. Set false to get counts-only summary for large batches. Default: true.
Behavior5/5

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

The description discloses the return format (structuredContent with detailed fields), explains overall_status values with meanings, and provides polling guidance. Annotations already declare readOnlyHint and idempotentHint, and the description adds behavioral context beyond those without contradiction.

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 well-structured with a summary line, argument list, return type explanation, status definitions, and a tip. Every sentence serves a purpose, and the structure aids readability without unnecessary verbosity.

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?

The description covers all aspects needed for a polling tool: purpose, parameters, return structure (including example fields), status interpretations, and a practical polling interval. Given the tool's complexity, it leaves no critical gaps.

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?

The schema already describes both parameters (100% coverage), but the description adds practical value for include_results, explaining the trade-off between full results and counts-only summary for large batches. This extra context justifies a score above baseline 3.

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 polls the status of an async batch job, specifically linking to replicate_batch_start. It uses precise language ('poll the status') and distinguishes itself from sibling tools like replicate_batch_start (starts jobs) and other status tools.

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 explicitly connects to replicate_batch_start, implying usage after starting a batch job. It also provides a concrete polling tip (every 10-30 seconds). However, it does not explicitly state when not to use it or contrast with single-prediction status tools, though the sibling context makes it 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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