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EliyahuAI
by EliyahuAI

get_job_status

Read-only

Quickly check the status of a research job using its job ID. Use this for one-shot inspections or as a fallback for long-running jobs.

Instructions

One-shot job status check. Prefer wait_for_job for tracking long-running jobs.

Use this for quick status inspections or as a fallback when wait_for_job is not appropriate. For polling loops, wait_for_job is more efficient — it holds the connection, emits live MCP progress notifications, and handles the multi-phase pipeline (table-maker → preview) automatically.

Key statuses: queued / processing → call wait_for_job instead of re-polling manually preview_complete → approve_validation (or refine_config) completed → get_results failed → check error field

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesSession ID / job ID returned by upload_file, start_table_validation, or start_table_maker.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Description confirms it is a one-shot, non-blocking read operation, consistent with readOnlyHint. It adds value by mapping statuses to recommended next actions, giving the agent a behavioral model.

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?

Concise and well-structured: opening sentence, usage guidance, bullet list of status mappings. Every sentence is informative with no filler.

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 one fully-described parameter, good annotations, and output schema, the description covers purpose, usage, and next steps. No gaps for a simple query tool.

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 single parameter job_id is fully described in the schema (100% coverage). The description adds context by listing where job_id comes from (upload_file, start_table_validation, start_table_maker), which aids in value selection.

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?

Description starts with 'One-shot job status check,' clearly stating the purpose. It contrasts with wait_for_job, making it easy to distinguish.

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

Usage Guidelines5/5

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

Explicitly says 'Prefer wait_for_job for tracking long-running jobs' and provides status-specific guidance (queued/processing → wait_for_job, preview_complete → approve_validation, etc.).

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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