Scan MCP

Scan‑MCP: A Lightweight Scanning Companion for AI Workflows

Scan‑MCP is a minimal Model Context Protocol server that turns any SANE‑compatible scanner into an AI‑friendly asset. It exposes a small set of typed tools—device discovery, option interrogation, and scan job orchestration—while handling the heavy lifting of file creation, batching, and multipage assembly. The result is a privacy‑first, on‑device solution that can be dropped into any MCP‑aware client (Claude Desktop, Windsurf, etc.) with a single configuration entry.

The core problem Scan‑MCP solves is the friction of integrating physical scanners into automated document pipelines. Traditional scanner utilities are often command‑line heavy, lack structured APIs, and produce raw image files that must be post‑processed. Scan‑MCP abstracts these details behind a JSON‑schema‑validated interface, automatically selects the best scanner backend (preferring ADF or duplex modes while excluding camera backends), and writes a tidy job directory containing TIFFs, a manifest, and an event log. This allows developers to focus on higher‑level logic—such as OCR or document classification—without wrestling with scanner quirks.

Key features include:

• Typed, deterministic outputs: Every tool returns JSON that conforms to a strict schema, enabling reliable parsing by AI assistants.
• Smart device selection: The server prefers scanners that support automatic document feeding or duplex scanning, while allowing the caller to override with a specific .
• Local‑first transport: By default the server communicates over stdio, keeping all data on the same machine and preserving privacy. An optional streamable HTTP transport (SSE) is available for LAN deployments, making it easy to host the scanner on a separate workstation.
• Robust defaults and persistence: The server remembers the last used device, applies sensible default options, and can be configured via environment variables to point at custom binaries or exclude certain backends.

Real‑world scenarios that benefit from Scan‑MCP include:

• Automated intake for legal or medical offices: A user can trigger a scan job through an AI assistant, receive the resulting TIFFs directly in their workflow, and immediately start document processing or archival.
• Remote scanning from a kiosk: By running Scan‑MCP on a dedicated machine and exposing the HTTP transport, clients can initiate scans from any device within the same network without exposing scanner credentials.
• Testing and CI pipelines: The flag allows developers to generate fake scans, enabling end‑to‑end testing of document pipelines without physical hardware.

Integration with AI workflows is straightforward: an MCP client calls , optionally specifying a device or letting the server auto‑select. The assistant receives streaming status updates via SSE (or stdio) and can present progress, errors, or the final artifact list to the user. Because all outputs are deterministic and typed, downstream agents can ingest the TIFFs or manifest without additional parsing logic.

In summary, Scan‑MCP transforms a legacy scanner into a first‑class AI tool. Its lightweight design, smart defaults, and seamless MCP integration make it an ideal component for any project that needs reliable, privacy‑preserving document capture within automated workflows.