FlexOrch MCP

Build Dataset

dataset.build

Package extracted records into a dataset for export (Step 4).

Triggers an async dataset build from a completed execution. Returns a job_id
immediately — poll with job.status until status='completed'. The completed
response includes dataset_id, which you pass to dataset.export to retrieve all
records as text. This step is required before calling dataset.export.

Args:
    execution_id: Execution ID from a completed data_process job
                  (from job.status or job.result).
    name: Dataset name. Auto-generated from the source filename if omitted.
    description: Optional description for this dataset.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No
`description`	No
`execution_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`job_id`	No
`status`	No
`isError`	No
`poll_hint`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 3, 2026

TDQS

A5/5.0

Behavior5/5

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

The description discloses the key behavioral trait that annotations do not: the operation is asynchronous. It states that it returns a job_id immediately, that job.status must be polled until 'completed', and that the completed response contains dataset_id. It also notes the prerequisite of a completed execution, adding meaningful behavioral context beyond the sparse 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?

The description is succinct yet information-dense. The one-line purpose is followed by a compact workflow sentence and a clear Arg list. Every sentence adds necessary context, and the most important usage constraint is front-loaded.

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 the async lifecycle, required inputs, optional parameters, dependency on prior execution, and downstream use with dataset.export. Since an output schema exists, the description does not need to enumerate return fields, and it provides the necessary integration details to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates. It explains execution_id as coming from a completed data_process job via job.status or job.result, name as auto-generated from the source filename when omitted, and description as optional. This gives an agent semantic understanding the schema alone lacks.

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-plus-resource statement: 'Package extracted records into a dataset for export (Step 4).' It clearly identifies the tool's role in the pipeline and distinguishes it from siblings like dataset.export and dataset.search by positioning it as the build step required before 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?

The description explains exactly when to use the tool: after a completed execution, before dataset.export. It also provides the full workflow: triggers async build, returns job_id, poll job.status until completed, then pass dataset_id to dataset.export. This is explicit usage guidance with sequential context, leaving little ambiguity.

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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