Classify documents, extract structured fields, mask PII, export JSONL/RAG datasets for AI agents.
FlexOrch MCP server exists for a simple reason — assistants are far more useful when they can act on FlexOrch directly instead of describing what you should do. Classify documents, extract structured fields, mask PII, export JSONL/RAG datasets for AI agents.
Connect Claude and other MCP-compatible agents to the FlexOrch document intelligence pipeline. Process documents, extract structured data, detect PII, and export LLM-ready datasets — all through natural language tool calls.
Once FlexOrch is connected, these are the calls the assistant has available:
document.process — Upload and process a document (PDF, DOCX, TXT, XLSX, HTML, XML, EML, JPG, PNG, TIFF)job.status — Poll a processing job until completed or failedjob.result — Get structured extracted fields from a completed jobdataset.build — Build a structured dataset from a completed executiondataset.search — Semantic search across indexed datasets (Pro+)dataset.export — Export a dataset as JSONL, CSV, JSON, XML, MD, or RAG (LangChain/LlamaIndex chunks)Cursor — The Cursor tool exposed by this serverYou will need one environment variable: FLEXORCH_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Installation goes through your MCP client rather than a global install: point it at flexorch-mcp on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. FlexOrch's toolset — document.process, job.status, job.result and 4 more — is a fair guide to whether it matches your workflow. It is maintained by dev-flexorch; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| document.process | Upload and process a document (PDF, DOCX, TXT, XLSX, HTML, XML, EML, JPG, PNG, TIFF) |
| 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) |
| Cursor | The Cursor tool exposed by this server. |
{
"mcpServers": {
"flexorch": {
"command": "uvx",
"args": ["flexorch-mcp"],
"env": {
"FLEXORCH_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| FLEXORCH_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.