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

How to connect

How to connect flexorch-mcp to Claude

The server address to paste into Claude settings:

https://flexorch-mcp--developer-ty82.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect flexorch-mcp to ChatGPT

The server address to paste into ChatGPT settings:

https://flexorch-mcp--developer-ty82.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect flexorch-mcp to Cursor

The server address to paste into Cursor settings:

https://flexorch-mcp--developer-ty82.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

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.