> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flexorch.com/llms.txt
> Use this file to discover all available pages before exploring further.

# MCP Server Overview

> Connect Claude and other AI agents to FlexOrch through the Model Context Protocol.

## What is flexorch-mcp?

`flexorch-mcp` is a stateless proxy that exposes the FlexOrch API as MCP (Model Context Protocol) tools. It lets Claude — and any MCP-compatible agent — process documents, extract structured data, detect PII, and export datasets through natural language tool calls.

**For developers writing code:** use [flexorch-sdk](/sdk/python/quickstart) (Python) or the [TypeScript SDK](/sdk/typescript/quickstart).
**For AI agents:** use `flexorch-mcp`.

```
pip install flexorch-mcp
```

***

## How it works

The MCP server acts as a thin proxy between the agent and the FlexOrch API:

```
Agent (Claude, etc.)
  → MCP tool call
    → flexorch-mcp server
      → FlexOrch API (api.flexorch.com)
        → Processed result back to agent
```

No document content or extracted data is stored by the MCP server. Every tool call goes directly to the FlexOrch API and returns the response. The server is **stateless**.

***

## Typical agent workflow

```
User: "Process this invoice and export it as JSONL."

Agent:
  1. process_document(file_url="https://...")     → job_id: 1234
  2. get_job_status(1234)                          → completed, execution_id: 567
  3. get_extraction_result(567)                    → vendor, total, IBAN [MASKED], due_date
  4. build_dataset(execution_id=567)               → job_id: 1235
  5. get_job_status(1235)                          → completed, dataset_id: 89
  6. export_dataset(89, format="jsonl")            → inline JSONL content
```

***

## 8 tools

| Tool                    | Description                                                                    |
| ----------------------- | ------------------------------------------------------------------------------ |
| `process_document`      | Download a document from a URL and submit it to the FlexOrch pipeline          |
| `get_job_status`        | Poll a job until completed or failed                                           |
| `get_extraction_result` | Retrieve structured extracted fields from a completed job                      |
| `build_dataset`         | Build a structured dataset from a completed execution                          |
| `search_documents`      | Full-text and semantic search across indexed datasets                          |
| `export_dataset`        | Export a dataset as JSONL, CSV, JSON, MD, XML, or RAG chunks                   |
| `dataset.index`         | Trigger semantic vector indexing for a dataset (Pro+)                          |
| `dataset.chunks`        | Retrieve LangChain/LlamaIndex-ready text chunks from an indexed dataset (Pro+) |

***

## Security

* **API key stays server-side** — `FLEXORCH_API_KEY` is read from the environment variable, never passed as a tool argument.
* **No local storage** — the MCP server is a stateless proxy. No document content or extracted data is written to disk.
* **PII masking applied by FlexOrch** — extracted fields are masked before the MCP server receives them. Raw PII never reaches the agent.
* **HTTPS only** — all communication with `api.flexorch.com` is encrypted.
* **URL scheme validation** — `process_document` only accepts `http://` and `https://` URLs. Local file paths (`file://`) are rejected.

***

## Links

* [PyPI](https://pypi.org/project/flexorch-mcp/) — `pip install flexorch-mcp`
* [GitHub](https://github.com/flexorch/flexorch-mcp) — MIT license
* [Quickstart](/mcp/quickstart) — Configure Claude Desktop in 2 minutes
* [Tool Reference](/mcp/tools) — Full parameter documentation