FlexOrch MCP

Get Job Status

job.status

Read-onlyIdempotent

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

Name	Required	Description	Default
`job_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`stage`	No
`job_id`	No
`reason`	No
`status`	No
`isError`	No
`degraded`	No
`pii_count`	No
`pii_found`	No
`poll_hint`	No
`row_count`	No
`dataset_id`	No
`pii_masked`	No
`document_id`	No
`has_dataset`	No
`dataset_name`	No
`execution_id`	No
`quality_grade`	No
`quality_score`	No
`pii_type_summary`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 3, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds meaningful behavioral details beyond those: polling cadence, terminal status values, and the presence of execution_id or dataset_id in the completed response. This informs the agent what to expect and how to proceed without contradicting the annotations.

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 compact and well-organized: main purpose first, then polling behavior, then downstream routing, then the argument. Every sentence adds useful information, and the Args section is cleanly separated.

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?

For a single-parameter polling tool with an output schema and readOnly/idempotent annotations, the description covers all necessary operational guidance: when to call, how to poll, terminal states, and what to do with the result. No critical context is missing.

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 only documents job_id as an integer, but the description adds critical provenance by stating it is 'returned by document.process or dataset.build'. This compensates for the 0% schema description coverage, though the explanation is brief and does not elaborate on validation or edge cases.

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 states a specific action—poll a job until it finishes—and identifies the exact triggering operations (document.process, dataset.build). It clearly separates this tool from the sibling job.result by explaining that job.result consumes data from the completed job status response.

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?

The description explicitly says when to call this tool ('after document.process or dataset.build'), how often to poll ('every 3–5 seconds'), and when to stop ('until status is 'completed' or 'failed''). It also routes downstream usage to job.result and dataset.export based on job type, giving clear context and alternatives.

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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