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get batch

get_batch
Read-onlyIdempotent

Fetch a batch job's live status, request counts, and output/error file IDs to check completion.

Instructions

Fetch one batch job with its status, request counts and output/error file ids refreshed live from the processing backend, to poll for completion. GET /v1/batches/{id} (API-key scope: read). Returns: { id, nebius_batch_id, endpoint, status, request_total, request_completed, request_failed, completion_window, billed_cost_usd, created_at, output_file_id, error_file_id, error } Notes: Best-effort live reconciliation: if the upstream status lookup fails the stored row is returned unchanged. Billing still happens in the background reconciler, not on this read. 404 "Batch not found" outside the workspace; 404 while the batch flag is off.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe batch id from POST /v1/batches or GET /v1/batches (the platform id, not the upstream batch id).
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TDQS

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, the description discloses important live-behavior details: best-effort reconciliation may return the stored row unchanged, billing is deferred to a background reconciler, and 404 behavior depends on workspace and feature flag. This gives the agent accurate expectations of side effects and failure modes.

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 information-dense but well organized: core purpose first, then endpoint/scope, return shape, and edge-case notes. Every sentence contributes useful operational context, though the return-field list makes it slightly longer than minimal.

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?

With no output schema, the description compensates by enumerating the return fields. It also covers API-key scope, live reconciliation semantics, billing side effects, and 404 edge cases. For a simple one-parameter read tool, this is fully sufficient for correct invocation.

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 already fully describes the one required parameter with 100% coverage, including the distinction between platform id and upstream batch id. The description adds no new parameter-level semantics beyond restating the endpoint path, so the 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 opens with a specific verb and resource—'Fetch one batch job'—and clearly states the live status/count/file-id retrieval purpose. It is immediately distinguishable from sibling tools like list_batches (bulk listing) and cancel_batch (mutation).

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 phrase 'to poll for completion' gives a clear, actionable use case, and the 404 notes explain when the tool will fail. It does not explicitly name alternatives or state when not to use it, but the single-resource framing sufficiently guides selection among siblings.

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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