Submit a document for processing — this is always the first step (Step 1 of 5).

Downloads the file from file_url, then submits it to FlexOrch for automatic classification, structured field extraction, PII detection/masking, and quality scoring. Processing is asynchronous — this tool returns immediately with a job_id. You MUST call job.status(job_id) every 3–5 seconds until status='completed' before calling job.result.

Args: file_url: Publicly accessible URL of the document (http/https only, max 50 MB). Supported: PDF, DOCX, TXT, XLSX, HTML, XML, EML, JPG, PNG, TIFF. mask_pii: Replace detected PII (names, IDs, emails, phone numbers) with [MASKED_TYPE] placeholders in all output. Default: true. document_type: Optional classification hint — FlexOrch auto-detects if omitted. Values: invoice, expense_report, purchase_order, sales_proposal, bank_statement, payroll.

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.

Read structured fields extracted from a completed document (Step 3).

Use the execution_id from a completed data_process job (job.status response). Returns document type, detected language, quality grade (A–D), PII summary, column list, and extracted field values. If no dataset has been built yet, the response includes a fields_hint guiding you to call dataset.build next. To retrieve all rows as a file, proceed to dataset.build → dataset.export.

Note: Masked fields appear as [MASKED_TYPE] placeholders — raw PII is never returned. Note: execution_id comes from data_process jobs only; dataset_build jobs use dataset_id.

Args: execution_id: Execution ID from the job.status completed response.

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.

Search across all indexed FlexOrch datasets by keyword or meaning.

Use this to find specific documents or records without processing a new file. Requires at least one dataset to exist. Structured search works on all plans. Semantic and hybrid modes require a Pro plan — a clear upgrade message is returned if the plan is insufficient. mode='auto' picks structured on free plans, hybrid on Pro+.

Args: query: Search query — natural language or keyword. Max 1000 characters. top_k: Number of results to return. Default: 5, max: 50. mode: Search strategy — auto (default), structured, semantic, hybrid. semantic and hybrid require Pro plan. document_type: Filter to a specific document type, e.g. invoice (optional). language: Filter by document language, ISO 639-1 code, e.g. en, de, tr (optional). quality_grade: Filter by quality grade: A, B, C, or D (optional).

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.

Trigger semantic indexing for a dataset — required before using dataset.chunks (Pro+ plan).

Starts an async indexing job that splits the dataset into RAG-ready text chunks, generates embeddings, and stores them for semantic search. Indexing is idempotent: calling it again on an already-indexed dataset re-indexes with fresh embeddings. Indexing typically completes in 10–60 seconds depending on dataset size. After indexing, use dataset.chunks(dataset_id) to retrieve the text chunks.

Args: dataset_id: ID of the built dataset to index (from job.status after dataset.build).

Retrieve LangChain/LlamaIndex-ready text chunks from an indexed dataset (Pro+ plan).

Returns paginated RAG chunks. Each chunk includes text, token count, and metadata (quality grade, PII masking status, document type, language). Use FlexOrchRetriever or FlexOrchReader from the flexorch-sdk for automatic pagination. The dataset must be indexed first via dataset.index(dataset_id).

Args: dataset_id: ID of the indexed dataset. min_quality: Minimum quality grade to include: A, B, C, or D. Default: B. Chunks with grade at or above this threshold are returned. Example: "B" returns grade A and B chunks. pii_masked_only: When true, return only chunks where PII was masked. Default: false. page: Page number, 1-indexed. Default: 1. page_size: Chunks per page, max 100. Default: 20.