flexorch-mcp

Convert unstructured business documents (PDF, DOCX, invoices, contracts, payroll sheets) into structured, LLM-ready datasets with automatic classification…

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What it can do

  • Document.Process: Submit a document for processing — Step 1 of 5. Returns job_id immediately; poll job.status until completed.
  • Job.Status: Poll a job until it finishes — Step 2. Call every 3-5s until status is completed or failed.
  • Job.Result: Read structured fields extracted from a completed document — Step 3.

What data it sees

Do you need an account

An API key from the service settings is required

Convert unstructured business documents (PDF, DOCX, invoices, contracts, payroll sheets) into structured, LLM-ready datasets with automatic classification, field extraction, PII masking, and quality scoring.

6 async tools:

  • process_document — submit document for processing
  • get_job_status — poll until complete
  • get_extraction_result — read structured fields + quality grade
  • build_dataset — package records into a named dataset
  • export_dataset — download as JSONL, RAG, CSV, or Markdown
  • search_documents — keyword + semantic search across datasets

Requires: FlexOrch API key — get one at app.flexorch.com/settings

Server tool list (8)

Raw names from tools/list. Only developers need these.

document.processSubmit a document for processing — Step 1 of 5. Returns job_id immediately; poll job.status until completed.
job.statusPoll a job until it finishes — Step 2. Call every 3-5s until status is completed or failed.
job.resultRead structured fields extracted from a completed document — Step 3.
dataset.buildPackage extracted records into a dataset for export — Step 4. Returns job_id; poll job.status until completed.
dataset.searchSearch across all indexed FlexOrch datasets by keyword or semantic meaning.
dataset.exportDownload all records from a built dataset as text — Step 5, final step.
dataset.indexTrigger semantic indexing for a dataset (Pro+ plan required). Must be called before dataset.chunks. Indexing is idempotent and typically completes in 10–60 seconds.
dataset.chunksRetrieve LangChain/LlamaIndex-ready text chunks from an indexed dataset (Pro+ plan required). Dataset must be indexed first via dataset.index.