Tool Reference

process_document

Download a document from a URL and submit it to the FlexOrch pipeline for classification, extraction, PII detection, and quality scoring. Parameters

Parameter	Type	Default	Description
`file_url`	string	required	Public HTTP/HTTPS URL of the document. Supports PDF, DOCX, TXT, XLSX, HTML, XML, EML, JPG, PNG, TIFF. Max 50 MB.
`mask_pii`	boolean	`true`	Mask detected PII in extracted fields. When `false`, raw values are returned.
`document_type`	string	`"auto"`	Classification hint. Auto-detected if omitted. Values: `invoice`, `expense_report`, `purchase_order`, `sales_proposal`, `bank_statement`, `payroll`, `auto`.

Returns

{
  "job_id": 1234,
  "status": "queued",
  "poll_hint": "Use get_job_status(1234) to check progress..."
}

On error:

{
  "isError": true,
  "error": "File exceeds size limit. Max 50 MB."
}

get_job_status

Poll a processing job until it completes or fails. Parameters

Parameter	Type	Default	Description
`job_id`	integer	required	Job ID returned by `process_document` or `build_dataset`.

Returns — completed (data_process)

{
  "status": "completed",
  "execution_id": 567,
  "quality_grade": "A",
  "quality_score": 91.0,
  "pii_found": true,
  "pii_masked": true,
  "pii_count": 3,
  "row_count": 12,
  "has_dataset": false,
  "poll_hint": "Use get_extraction_result(567) to retrieve fields."
}

Returns — completed (dataset_build)

{
  "status": "completed",
  "dataset_id": 89,
  "dataset_name": "q1_invoices",
  "row_count": 12,
  "poll_hint": "Use export_dataset(89, format='jsonl') to download."
}

Returns — running

{
  "status": "running",
  "stage": "privacy"
}

Pipeline stages: extract → privacy → quality → dataset. Returns — failed

{
  "status": "failed",
  "reason": "UNSUPPORTED_FILE"
}

get_extraction_result

Retrieve structured extracted fields from a completed processing job. Returns up to 100 records inline — for larger documents, use export_dataset. Parameters

Parameter	Type	Default	Description
`execution_id`	integer	required	Execution ID from `get_job_status` completed response.

Returns

{
  "execution_id": 567,
  "document_type": "invoice",
  "detected_language": "tr",
  "quality": { "grade": "A", "score": 91.0, "warnings": [] },
  "privacy": { "pii_findings_count": 3, "pii_masked": true },
  "row_count": 1,
  "columns": ["invoice_number", "vendor_name", "total_amount", "currency", "due_date"],
  "fields": [
    {
      "invoice_number": "FTR-2024-001",
      "vendor_name": "[MASKED_NAME]",
      "total_amount": 12450.00,
      "currency": "EUR",
      "due_date": "2024-01-15"
    }
  ]
}

When more than 100 records exist:

{
  "has_more": true,
  "has_more_hint": "Showing first 100 of 847 records. Call export_dataset(89, format='jsonl') to retrieve all records at once."
}

When no dataset is built yet (records array empty):

{
  "fields_hint": "Field values are available after building a dataset. Call build_dataset(execution_id=567) then export_dataset(dataset_id, format)."
}

Masked fields use the [MASKED_TYPE] placeholder format — e.g. [MASKED_NAME], [MASKED_EMAIL], [MASKED_NATIONAL_ID_TR]. Raw PII values are never exposed.

build_dataset

Build a structured, exportable dataset from a completed execution. Parameters

Parameter	Type	Default	Description
`execution_id`	integer	required	Execution ID from `get_job_status` or `get_extraction_result`.
`name`	string	`""`	Dataset name. Auto-generated if omitted.
`description`	string	`""`	Optional description.

Returns

{
  "job_id": 1235,
  "status": "queued",
  "poll_hint": "Use get_job_status(1235) to check build progress..."
}

Poll get_job_status until status is "completed" — the response will include dataset_id.

search_documents

Search across all indexed datasets using structured keyword matching or semantic vector search. Parameters

Parameter	Type	Default	Description
`query`	string	required	Search query. Max 1000 characters.
`top_k`	integer	`5`	Number of results to return. Min 1, max 50.
`mode`	string	`"auto"`	Search mode: `auto`, `structured`, `semantic`, `hybrid`. Semantic and hybrid require Pro plan.
`document_type`	string	`""`	Filter by document type (optional).
`language`	string	`""`	Filter by language ISO 639-1 code, e.g. `"tr"`, `"en"` (optional).
`quality_grade`	string	`""`	Filter by quality grade: `A`, `B`, `C`, `D` (optional).

Returns

{
  "results": [
    {
      "chunk_id": "abc-123",
      "dataset_id": 89,
      "dataset_name": "q1_invoices",
      "score": 0.94,
      "text": "Invoice FTR-2024-001 from [MASKED_NAME] for €12,450...",
      "metadata": {
        "doc_type": "invoice",
        "language": "tr",
        "quality_grade": "A",
        "mode": "structured"
      }
    }
  ],
  "total_results": 1,
  "mode": "structured",
  "query": "invoice 2024"
}

mode="semantic" and mode="hybrid" require a Pro plan or above. Trial and Starter plans return a PLAN_UPGRADE_REQUIRED error for these modes. Use mode="auto" to fall back to structured search automatically.

export_dataset

Export a built dataset and return its full content as text. Parameters

Parameter	Type	Default	Description
`dataset_id`	integer	required	Dataset ID from `get_job_status` (dataset_build completed).
`format`	string	`"jsonl"`	Export format. Supported: `jsonl`, `csv`, `json`, `md`, `xml`, `rag`.

Supported formats

Format	Best for
`jsonl`	LLM fine-tuning (OpenAI, Mistral)
`csv`	Spreadsheet analysis, Pandas
`json`	General structured data
`md`	Markdown-based RAG pipelines
`xml`	Enterprise integrations
`rag`	LangChain / LlamaIndex chunk arrays

Binary formats (parquet, hf) are not supported via MCP — download them directly from GET /v1/datasets/{id}/export/{format}. Returns

{
  "dataset_id": 89,
  "format": "jsonl",
  "filename": "q1-invoices.jsonl",
  "content": "{\"invoice_number\":\"FTR-2024-001\",\"total_amount\":12450.0}\n...",
  "byte_count": 4821
}

dataset.index

Requires a Pro or Enterprise plan.

Trigger semantic vector indexing for a built dataset. Must be called before dataset.chunks. Indexing is idempotent — calling it again on an already-indexed dataset is safe. Parameters

Parameter	Type	Default	Description
`dataset_id`	integer	required	ID of the built dataset (from `get_job_status` after `build_dataset`).

Returns

{
  "dataset_id": 89,
  "status": "indexing",
  "message": "Indexing started. Check back in 10–60 seconds.",
  "index_hint": "Use dataset.chunks(89) after indexing completes to retrieve RAG-ready chunks."
}

On plan error:

{
  "isError": true,
  "error": "Semantic indexing requires a Pro plan. Upgrade at app.flexorch.com/settings."
}

Indexing typically completes in 10–60 seconds depending on dataset size. There is no async status poll for this tool — wait a few seconds and proceed to dataset.chunks.

dataset.chunks

Requires a Pro or Enterprise plan. Index the dataset first with dataset.index.

Retrieve LangChain/LlamaIndex-ready text chunks from an indexed dataset. Supports quality filtering and pagination. Parameters

Parameter	Type	Default	Description
`dataset_id`	integer	required	ID of the indexed dataset.
`min_quality`	string	`"B"`	Minimum quality grade to include. `"B"` returns A and B grade chunks.
`pii_masked_only`	boolean	`false`	When `true`, only return chunks where PII was detected and masked.
`page`	integer	`1`	Page number (1-indexed).
`page_size`	integer	`20`	Chunks per page, max 100.

Returns

{
  "dataset_id": 89,
  "chunks": [
    {
      "chunk_id": "abc-001",
      "chunk_index": 0,
      "text": "Invoice FTR-2024-001 from [MASKED_NAME] for €12,450...",
      "token_count": 84,
      "metadata": {
        "quality_grade": "A",
        "pii_masked": true,
        "doc_type": "invoice",
        "language": "tr"
      }
    }
  ],
  "chunk_count": 20,
  "total": 47,
  "page": 1,
  "page_size": 20,
  "has_more": true
}

On plan error:

{
  "isError": true,
  "error": "Chunk listing requires a Pro plan. Upgrade at app.flexorch.com/settings."
}

RAG workflow with MCP:

process_document → get_job_status → build_dataset → get_job_status
→ dataset.index → (wait ~30s) → dataset.chunks → embed + query

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process_document

get_job_status

get_extraction_result

build_dataset

search_documents

export_dataset

dataset.index

dataset.chunks

​process_document

​get_job_status

​get_extraction_result

​build_dataset

​search_documents

​export_dataset

​dataset.index

​dataset.chunks

process_document

get_job_status

get_extraction_result

build_dataset

search_documents

export_dataset

dataset.index

dataset.chunks