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Loupe MCP

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Loupe MCP

Loupe is an MCP server that reads documents for AI agents. It turns PDFs, scans, photos and Office files into text, at one of two quality levels:

Quality

How

Result

fast

Azure Document Intelligence, layout model

Markdown per page

accurate

Document Intelligence (high-resolution OCR), the PDF's text layer and form fields, and an image of the page, reconciled per page by a vision model

HTML per page; photos and drawings described, checkboxes, handwriting and strike-through marked

Accepted inputs: PDF; JPEG, PNG, BMP, TIFF (multi-page), HEIC/HEIF; DOCX, DOCM, XLSX, PPTX; HTML. Types are detected by content, never by name.

Agents upload files from their sandbox with curl to a signed URL, submit them as one batch job, and read the text page by page in context or download it. Jobs run independently of the MCP connection, report progress per document, and continue on another replica if one restarts. One Loupe deployment serves many projects.

The full design is in docs/CONCEPT.md.

Tools

Tool

Purpose

create_upload

Creates an upload and returns a signed URL; PUT each file to <uploadUrl><name>

submit

Starts a job over an upload's files at quality fast or accurate, optionally with a language hint and a page selection

get_job

Status, progress per document, page methods, and one manifest URL for downloads

read_result

Whole pages of one document's transcript, as many as fit into a window

cancel_job

Stops a job; documents already read stay readable

A typical session:

create_upload()                                   → u_8sLx2…, uploadUrl
submit("u_8sLx2…", "accurate", language: "de")    → j_4fKq9…, 120 documents
get_job("j_4fKq9…", wait: 10)                     → running, 37 of 120 succeeded
read_result("j_4fKq9…", "contract.pdf")           → pages 1–4
U="<uploadUrl>"; for f in scans/*; do curl -fsS -T "$f" "$U"; done
M="<download.manifestUrl>"; B="${M%/manifest.json}"
curl -fsS "$M" | jq -r '.documents[] | select(.status == "succeeded") | .path' \
  | xargs -P4 -I{} curl -fsS --create-dirs -o {} "$B/{}"

Related MCP server: LandingAI ADE MCP Server

Run locally (stdio)

Requires Node.js 24, pnpm, and an Azure Document Intelligence resource plus an Azure OpenAI deployment of a vision model.

pnpm install
cp .env.example .env.loupe   # add your endpoints and keys
node src/index.ts --env-file .env.loupe

Register it with Claude Code:

claude mcp add loupe -- node /path/to/loupe-mcp/src/index.ts --env-file /path/to/.env.loupe

In stdio mode, the store is ./.loupe/store, and upload and download URLs point to a small HTTP endpoint on 127.0.0.1 that the server starts alongside.

With Docker (the published image runs the HTTP transport by default):

docker run -i --rm --env-file .env.loupe \
  -e LOUPE_TRANSPORT=stdio -e LOUPE_HOST=0.0.0.0 -e LOUPE_PORT=8765 \
  -e LOUPE_PUBLIC_URL=http://127.0.0.1:8765 -p 127.0.0.1:8765:8765 \
  ghcr.io/coreprocess/loupe-mcp:0.1.0

Deploy

Loupe runs as one shared container deployment, protected by Microsoft Entra ID (OAuth 2.1, MCP authorization spec): directly when the host is on a domain verified in the tenant, otherwise through Loupe's built-in authorization server, which signs users in at Entra (LOUPE_AUTH=entra-broker). See docs/deployment.md, the configuration reference in docs/configuration.md, and the Terraform module in terraform/.

Development

pnpm lint && pnpm typecheck && pnpm test   # unit tests, no Docker
pnpm test:integration                       # Azurite, two replicas, curl; needs Docker

The tests run against mocks of Document Intelligence and Azure OpenAI; test documents are generated.

License

MIT

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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