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flexorch

flexorch-mcp

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Get Job Status

job.status
Read-onlyIdempotent

Poll a job until it completes or fails. Retrieve the execution or dataset ID for the next step in the workflow.

Instructions

Poll a job until it finishes — call this after document.process or dataset.build (Step 2).

Call repeatedly every 3–5 seconds until status is 'completed' or 'failed'. For data_process jobs: the completed response includes execution_id — pass it to job.result. For dataset_build jobs: the completed response includes dataset_id — pass it to dataset.export.

Args: job_id: Job ID returned by document.process or dataset.build.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
stageNo
job_idNo
reasonNo
statusNo
isErrorNo
pii_countNo
pii_foundNo
poll_hintNo
row_countNo
dataset_idNo
pii_maskedNo
has_datasetNo
dataset_nameNo
execution_idNo
quality_gradeNo
quality_scoreNo
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds polling interval and completion behavior. No contradictions; it aligns with annotation hints.

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, front-loaded summary followed by bullet points for usage details. Every sentence is informative.

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 existence of an output schema, the description appropriately focuses on usage and behavior. Minor gap: lacks guidance on timeout or handling errors (e.g., if status never reaches terminal state).

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?

Schema has one required integer parameter (job_id) with 0% description coverage. The description explains job_id is returned by the preceding tools, adding context. It could clarify that job_id is an integer, but the schema already specifies type.

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 tool polls a job until completion, names the specific preceding tools (document.process, dataset.build), and distinguishes from siblings by describing the data flow (passing IDs to job.result or dataset.export).

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 instructs to call repeatedly every 3-5 seconds until status is 'completed' or 'failed', and tells what to do with the returned IDs for different job types. No alternative usage is needed.

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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