FlexOrch MCP

Export Dataset

dataset.export

Read-onlyIdempotent

Download all records from a built dataset as text (Step 5 — final step).

Returns the complete dataset content as a UTF-8 string directly in the response —
no file download or separate URL needed. Call get_job_status after build_dataset
and wait for status='completed' before calling this tool. Use the dataset_id from
that completed response.

Format guide: jsonl = LLM fine-tuning, rag = LangChain/LlamaIndex chunks,
csv = spreadsheets, md = human-readable, xml = structured interchange.
Binary formats (parquet, hf) cannot be returned via MCP — export them from
the FlexOrch dashboard directly.

Args:
    dataset_id: Dataset ID from the get_job_status completed build response.
    format: Text export format — jsonl, csv, json, md, xml, rag. Default: jsonl.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`format`	No		jsonl
`dataset_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`format`	No
`content`	No
`isError`	No
`filename`	No
`byte_count`	No
`dataset_id`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 3, 2026

TDQS

A4.8/5.0

Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable context beyond this: the response is a UTF-8 string returned directly in the response, no file download or separate URL is involved, and binary formats cannot be returned via MCP.

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 well-structured and front-loaded with the core purpose. Sections for return behavior, usage sequence, format guide, and parameters all add value without redundancy or 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?

For a two-parameter tool with an output schema, the description is fully complete: it covers prerequisites, workflow order, format selection, return type, and known limitations. An agent has everything needed to invoke the tool correctly and interpret the result.

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?

Schema description coverage is 0%, so the description carries the full burden for parameter meaning. It explains dataset_id as coming from a completed get_job_status response and enumerates the text formats with their intended use cases, which is far more useful than the raw schema.

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 downloads all records from a built dataset as text and identifies it as Step 5, the final step. This distinguishes it from siblings like dataset.build, dataset.chunks, and dataset.search.

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 describes the required sequence: call get_job_status after build_dataset, wait for status='completed', then use the returned dataset_id. It also explains which format to choose per use case and directs users to the dashboard for binary formats that MCP cannot return.

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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