scan-mcp
MCP ServerFree** - Minimal MCP server for scanner capture (ADF/duplex/page-size); typed tools; JSON Schema–validated I/O; multipage assembly; Node 22 + SANE.
Capabilities6 decomposed
sane-based physical document scanning with hardware control
Medium confidenceInterfaces with SANE (Scanner Access Now Easy) daemon to directly control physical scanner hardware, enabling capture of documents via ADF (automatic document feeder) or flatbed with configurable page sizes and duplex modes. Uses Node.js bindings to the SANE C library, abstracting low-level scanner device enumeration, parameter negotiation, and pixel-stream capture into typed MCP tool calls with JSON Schema validation for all inputs and outputs.
Direct SANE daemon integration via Node.js bindings with typed MCP tool schema validation, enabling AI agents to control physical scanner hardware with full parameter negotiation (duplex, page size, ADF) rather than wrapping command-line tools or REST APIs
More direct and lower-latency than cloud-based scanning APIs (no network round-trips) and more flexible than simple CLI wrappers, with full type safety and schema validation for all scanner parameters
multi-page document assembly and sequencing
Medium confidenceOrchestrates sequential scanning of multiple pages through SANE, collecting individual page captures and assembling them into a coherent multi-page document output. Implements page ordering, duplex mode handling (front/back page pairing), and optional page numbering or metadata tagging. Uses in-memory buffering to track page sequence and supports both ADF auto-feed and manual page-by-page scanning workflows.
Implements page assembly as a stateful MCP tool that maintains scan sequence across multiple tool invocations, with explicit duplex mode handling that pairs front/back pages rather than treating them as separate documents
More intelligent than simple page concatenation — understands duplex scanning semantics and can pair front/back pages automatically, vs. generic image stitching tools that treat pages as independent
json schema–validated scanner parameter configuration
Medium confidenceExposes all scanner parameters (page size, resolution, color mode, duplex, ADF settings) through strictly typed MCP tools with JSON Schema validation on both input and output. Validates parameter values against scanner hardware capabilities before sending to SANE, preventing invalid configurations and providing clear error messages for unsupported combinations. Uses schema-based function calling to ensure AI agents can only request valid scanner states.
Implements JSON Schema validation as a first-class MCP pattern for hardware control, ensuring all scanner parameters are validated before SANE invocation rather than relying on SANE error handling alone
Provides validation at the MCP layer (before hardware calls) vs. reactive error handling, reducing failed hardware operations and enabling AI agents to understand valid parameter ranges upfront
scanner device enumeration and capability discovery
Medium confidenceQueries the SANE daemon to enumerate all connected scanner devices and discover their capabilities (supported page sizes, color modes, resolutions, duplex support, ADF availability). Returns structured metadata about each device including manufacturer, model, and available parameters. Uses SANE device enumeration API to build a capability registry that informs parameter validation and user-facing configuration options.
Exposes SANE device enumeration as a typed MCP tool with structured capability metadata, enabling AI agents to query available hardware and adapt workflows dynamically rather than requiring pre-configured device lists
More dynamic than hardcoded device lists and more structured than raw SANE CLI output, providing AI agents with machine-readable capability data for intelligent device selection
adf (automatic document feeder) mode scanning with auto-feed control
Medium confidenceConfigures and controls SANE scanner ADF mode, enabling automatic page feeding for high-volume document capture. Handles ADF-specific parameters like auto-feed enable/disable, page detection, and jam recovery. Abstracts ADF state management so AI agents can request 'scan N pages with ADF' without managing individual page feed commands. Supports both continuous ADF feeding and page-by-page manual feed fallback.
Abstracts ADF state and feed control as high-level MCP operations ('scan N pages with ADF') rather than exposing low-level feed commands, enabling AI agents to request batch scanning without managing mechanical feed state
Higher-level abstraction than raw SANE ADF parameters, with built-in handling for ADF-specific concerns like jam detection and page counting, vs. generic scanning tools that treat ADF as just another parameter
typed mcp tool schema generation and validation
Medium confidenceGenerates strongly-typed MCP tool definitions with JSON Schema for all scanner operations, ensuring type safety across the MCP protocol boundary. Implements schema-based function calling that validates all inputs against defined schemas before execution and validates outputs to ensure consistency. Uses TypeScript or similar for compile-time type checking, with runtime schema validation as a safety layer. Enables IDE autocomplete and type hints for MCP clients calling scanner tools.
Implements end-to-end typed tool definitions with compile-time TypeScript types and runtime JSON Schema validation, enabling both IDE-level type safety and runtime guardrails for MCP scanner tools
Combines compile-time type checking with runtime validation, vs. either pure TypeScript (no runtime safety) or pure schema validation (no IDE hints), providing defense-in-depth for hardware control
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with scan-mcp, ranked by overlap. Discovered automatically through the match graph.
Ripcord
Revolutionize document management with AI-driven, robotic data...
Qwen: Qwen3 VL 32B Instruct
Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...
Qwen: Qwen3 VL 235B A22B Instruct
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...
LlamaIndex
A data framework for building LLM applications over external data.
Bizagi
Streamline processes, build apps, integrate AI—effortlessly with...
Qwen: Qwen3 VL 235B A22B Thinking
Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math....
Best For
- ✓developers building document processing workflows with LLM agents
- ✓teams automating office document capture and digitization
- ✓solo developers integrating physical scanners into AI-driven pipelines
- ✓document digitization workflows processing multi-page batches
- ✓teams automating office scanning with duplex-capable hardware
- ✓developers building document management integrations with AI agents
- ✓teams building AI agents that control hardware and need safety guardrails
- ✓developers integrating scanners into multi-step workflows where parameter errors are costly
Known Limitations
- ⚠Requires SANE daemon running on the host system — not portable to environments without scanner hardware or SANE support
- ⚠Limited to scanner capabilities exposed by SANE — some proprietary scanner features may not be accessible
- ⚠No built-in image compression or format conversion — outputs raw pixel data requiring downstream processing
- ⚠Single-threaded scanning — cannot parallelize multiple document captures across devices
- ⚠In-memory buffering limits practical document size — very large documents (1000+ pages) may exhaust memory
- ⚠No built-in page reordering or rotation correction — assumes scanner outputs pages in correct order
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
** - Minimal MCP server for scanner capture (ADF/duplex/page-size); typed tools; JSON Schema–validated I/O; multipage assembly; Node 22 + SANE.
Categories
Alternatives to scan-mcp
Are you the builder of scan-mcp?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →