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flexorch-mcp
<!-- mcp-name: io.github.dev-flexorch/flexorch-mcp -->
MCP server for FlexOrch — SDK for machines.
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.
What this is
flexorch-mcp is a thin proxy that exposes the FlexOrch API as MCP tools. All processing happens on FlexOrch's managed infrastructure. A FlexOrch account and API key are required.
For humans writing code: use flexorch-sdk (Python) or flexorch-sdk-js (TypeScript). For agents: use this package.
Tools
| Tool | Description |
|---|---|
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+) |
Installation
pip install flexorch-mcpRequires Python 3.10+.
Configuration
Claude Desktop
Add to your Claude Desktop config file (create it if it doesn't exist):
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"flexorch": {
"command": "flexorch-mcp",
"env": {
"FLEXORCH_API_KEY": "dfx_your_key_here"
}
}
}
}Cursor
Add to your Cursor MCP config:
{
"flexorch": {
"command": "flexorch-mcp",
"env": {
"FLEXORCH_API_KEY": "dfx_your_key_here"
}
}
}OpenAI Codex
Add to ~/.codex/config.toml:
[mcp_servers.flexorch]
command = "uvx"
args = ["flexorch-mcp"]
[mcp_servers.flexorch.env]
FLEXORCH_API_KEY = "dfx_your_key_here"Get your API key from app.flexorch.com/settings.
Verify connection
flexorch-mcp --check
# → FlexOrch API key: dfx_xxx*** ✓
# → Connection: OK (api.flexorch.com)
# → Plan: Starter (1,200 credits/mo)
# → Tools: 9 registeredExample agent workflow
User: "Process this invoice and export it as JSONL for fine-tuning."
Agent:
1. document.process(file_url="https://...") → job_id: 1234
2. job.status(1234) → completed, execution_id: 567
3. job.result(567) → vendor, total, date, PII masked
4. dataset.build(execution_id=567) → job_id: 1235
5. job.status(1235) → completed, dataset_id: 89
6. dataset.export(89, format="jsonl") → inline JSONL contentPlan limits
All FlexOrch plan limits apply to MCP tool calls. Credits are consumed per document processed.
| Plan | Credits/mo | Semantic search |
|---|---|---|
| Trial | 1,200 (30 days) | — |
| Starter | 1,200 | — |
| Pro | 6,000 | ✓ |
| Enterprise | Custom | ✓ |
Security
- API key is read from the
FLEXORCH_API_KEYenvironment variable — never passed as a tool argument - No data is stored or cached by this server — stateless proxy
- PII masking is applied by FlexOrch's pipeline before results are returned
- All communication with
api.flexorch.comuses HTTPS
Related
- flexorch-audit — Standalone PII detection and document quality scoring (no account required)
- flexorch-sdk — Python SDK for developers
- flexorch-sdk-js — TypeScript SDK for developers
- docs.flexorch.com — Full documentation
License
MIT — see LICENSE.