# flexorch/flexorch-mcp [Health: Active]

**Category:** 📊 Data Platforms  
**Repository:** https://github.com/flexorch/flexorch-mcp  
**GitHub Stars:** 1  
**Views:** 3  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/flexorch-flexorch-mcp

## Description
Convert unstructured business documents (PDF, DOCX, invoices, contracts, payroll) into structured, LLM-ready datasets with automatic classification, field extraction, PII masking (10+ locales), and quality scoring. 6 async MCP tools covering the full pipeline: submit → poll → extract → build → export JSONL/RAG/CSV. Install: pip install flexorch-mcp.

## Tools
Capabilities this server exposes over MCP:

- **document.process** — Upload and process a document (PDF, DOCX, TXT, XLSX, HTML, XML, EML, JPG, PNG, TIFF)
- **document.reprocess** — Re-queue an already-uploaded document through the pipeline
- **job.status** — Poll a processing job until completed or failed
- **job.result** — Get structured extracted fields from a completed job
- **dataset.build** — Build a structured dataset from a completed execution
- **dataset.search** — Semantic search across indexed datasets (Pro+)
- **dataset.export** — Export a dataset as JSONL, CSV, JSON, XML, MD, or RAG (LangChain/LlamaIndex chunks)
- **dataset.index** — Trigger semantic vector indexing for a dataset (Pro+)
- **dataset.chunks** — Retrieve paginated RAG-ready text chunks from an indexed dataset (Pro+)

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):

```json
"mcpServers": {
  "flexorch-mcp": {
    "command": "uvx",
    "args": ["flexorch-mcp"],
    "env": {
      "FLEXORCH_API_KEY": ""
    }
  }
}
```

**Requires environment variables:** `FLEXORCH_API_KEY` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What flexorch/flexorch-mcp MCP server does

The flexorch/flexorch-mcp MCP server exposes the FlexOrch API as MCP tools for document intelligence workflows. It is intended for agents rather than application code: developers building directly against FlexOrch can use the separate Python or TypeScript SDKs referenced by the project.

The pipeline accepts PDF, DOCX, TXT, XLSX, HTML, XML, EML, JPG, PNG, and TIFF files. Processing can classify documents, extract structured fields, mask personally identifiable information across more than 10 locales, and provide quality scoring. The resulting data can be assembled into datasets for downstream model or retrieval workflows.

## How it works

Processing is asynchronous. An agent first submits a document with `document.process`, then polls the resulting job using `job.status`. Once processing completes, `job.result` returns the extracted fields. `document.reprocess` can send an already-uploaded document through the pipeline again.

A completed execution can be passed to `dataset.build` to create a structured dataset. Datasets can be exported in JSONL, CSV, JSON, XML, or Markdown formats. The export operation also supports RAG output containing chunks intended for LangChain or LlamaIndex workflows.

For plans that include semantic features, `dataset.index` starts vector indexing, `dataset.search` performs semantic search over indexed datasets, and `dataset.chunks` returns paginated RAG-ready chunks.

## Setup and configuration

Install the package with Python 3.10 or newer:

```bash
pip install flexorch-mcp
```

The flexorch/flexorch-mcp MCP server requires a FlexOrch account and an API key. Set the key in the `FLEXORCH_API_KEY` environment variable; it is not supplied as a tool argument. The documented executable is `flexorch-mcp`.

Claude Desktop and Cursor configurations run the executable directly and provide the environment variable. An OpenAI Codex example uses `uvx flexorch-mcp` with the same variable. The repository provides a `flexorch-mcp --check` command to verify the key, API connection, plan, and registered tools.

## Tools and capabilities

The available tools cover the full document-to-dataset flow:

- `document.process` uploads and processes supported files.
- `document.reprocess` re-queues an uploaded document.
- `job.status` checks whether an asynchronous job completed or failed.
- `job.result` retrieves structured fields from a completed job.
- `dataset.build` creates a dataset from a completed execution.
- `dataset.export` returns a dataset in several structured or RAG-oriented formats.
- `dataset.index`, `dataset.search`, and `dataset.chunks` provide vector indexing, semantic search, and chunk retrieval on eligible plans.

## Limitations and notes

The server is a stateless proxy: it does not store or cache document data. Processing takes place on FlexOrch-managed infrastructure, and communication with `api.flexorch.com` uses HTTPS. PII masking is applied by FlexOrch before results are returned.

Usage follows the FlexOrch plan rules, with credits consumed per processed document. Trial and Starter plans do not include semantic search; Pro includes it, while Enterprise limits are custom. The flexorch/flexorch-mcp MCP server therefore requires a plan with the relevant feature for indexing, search, or chunk retrieval. The project is licensed under MIT.

_Full upstream README: https://allmcps.com/mcp/flexorch-flexorch-mcp/readme_