Nutrient DWS MCP Server

Give AI agents the power to generate, read, extract, process, and sign documents.

Description

A Model Context Protocol (MCP) server that connects AI assistants to the Nutrient Document Web Service (DWS) Processor and Data Extraction APIs — enabling document creation, editing, conversion, digital signing, OCR, and redaction, plus structured data extraction (typed JSON with bounding boxes and confidence, or schema-guided field extraction with per-field citations) through natural language.

Features

• Local stdio MCP server for Claude Desktop and other MCP-compatible clients
• Browser-based OAuth on the first request that uses the Nutrient API, with optional API-key fallback for CI and headless environments
• Document conversion, OCR, redaction, watermarking, annotation flattening, and digital signing (Processor API)
• Data extraction (Data Extraction API): parse whole documents to Markdown or spatial JSON, then pull named fields into a JSON schema you define, with per-field citations. Four parse modes: text (1 credit/page, no OCR), structure (1.5), understand (9, the default), agentic (18, VLM)
• Sandbox-aware local file handling with explicit output paths
• Read-only account lookup for DWS credits and usage

What You Can Do

Once configured, you (or your AI agent) can process documents through natural language:

You: "Merge report-q1.pdf and report-q2.pdf into a single document" AI: "Done! I've merged both reports into combined-report.pdf (24 pages total)."

You: "Redact all social security numbers and email addresses from application.pdf" AI: "I found and redacted 5 SSNs and 3 email addresses. The redacted version is saved as application-redacted.pdf."

You: "Digitally sign this contract with a visible signature on page 3" AI: "I've applied a PAdES-compliant digital signature to contract.pdf. The signed document is saved as contract-signed.pdf."

You: "Convert this PDF to markdown" AI: "Here's the markdown content extracted from your document..."

You: "OCR this scanned document in German and extract the text" AI: "I've processed the scan with German OCR. Here's the extracted text..."

You: "Pull the vendor, invoice number, total, and due date out of invoice-0341.pdf, with citations" AI: "Here are the four fields as JSON. Each value cites the page and bounding box it came from..."

Installation

Install it from Claude Desktop Settings -> Extensions if you are using Claude Desktop. If you are developing locally, use the manual setup below.

The Claude Desktop MCPB extension defaults its sandbox directory to ~/Documents/Nutrient. You can change that directory in the extension settings. Clearing the field starts the server without sandbox restrictions, so file operations can use any path visible to your user account.

1. Create a Nutrient Account

Sign up for free at nutrient.io/api.

For local desktop use, the recommended path is to omit NUTRIENT_DWS_API_KEY and complete the browser sign-in flow on the first request that uses the Nutrient API. For CI, headless environments, or scripted setups, create an API key in the dashboard and set NUTRIENT_DWS_API_KEY.

2. Configure Your AI Client

Choose your platform and add the configuration:

{
  "mcpServers": {
    "nutrient-dws": {
      "command": "npx",
      "args": ["-y", "@nutrient-sdk/dws-mcp-server"],
      "env": {
        "SANDBOX_PATH": "/your/sandbox/directory",
        // "C:\\your\\sandbox\\directory" for Windows
        // Optional for CI or headless usage:
        // "NUTRIENT_DWS_API_KEY": "YOUR_API_KEY_HERE"
      },
    },
  },
}

{
  "mcpServers": {
    "nutrient-dws": {
      "command": "npx",
      "args": ["-y", "@nutrient-sdk/dws-mcp-server"],
      "env": {
        "SANDBOX_PATH": "/your/project/documents",
        // "C:\\your\\project\\documents" for Windows
        // Optional for CI or headless usage:
        // "NUTRIENT_DWS_API_KEY": "YOUR_API_KEY_HERE"
      },
    },
  },
}

{
  "mcpServers": {
    "nutrient-dws": {
      "command": "npx",
      "args": ["-y", "@nutrient-sdk/dws-mcp-server"],
      "env": {
        "SANDBOX_PATH": "/your/sandbox/directory",
        // "C:\\your\\sandbox\\directory" for Windows
        // Optional for CI or headless usage:
        // "NUTRIENT_DWS_API_KEY": "YOUR_API_KEY_HERE"
      },
    },
  },
}

{
  "servers": {
    "nutrient-dws": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@nutrient-sdk/dws-mcp-server"],
      "env": {
        "SANDBOX_PATH": "${workspaceFolder}",
        // Optional for CI or headless usage:
        // "NUTRIENT_DWS_API_KEY": "YOUR_API_KEY_HERE"
      },
    },
  },
}

SANDBOX_PATH=/your/path npx @nutrient-sdk/dws-mcp-server

# Optional for CI or headless usage:
NUTRIENT_DWS_API_KEY=your_key SANDBOX_PATH=/your/path npx @nutrient-sdk/dws-mcp-server

3. Restart Your AI Client

Restart the application to pick up the new MCP server configuration.

4. Start Processing Documents

Place documents in your sandbox directory and use explicit file names or paths in prompts. Explicit paths are safer and more reliable than vague file-browsing requests.

Available Tools

Prompts

• sign_and_watermark — Add a text watermark to a document, then digitally sign the watermarked PDF.
• extract_document_fields — Extract named fields into a JSON object, optionally retaining citations in a file.
• redact_pii — Detect and permanently redact personally identifiable information from a document.
• parse_for_rag — Parse a document as Markdown for retrieval-augmented generation and search indexing.
• office_to_pdfa — Convert an Office document to an archival PDF/A file.

Document Processor Capabilities

Data Extraction

The parse_document and extract_fields tools wrap the DWS Data Extraction API and authenticate as follows:

• OAuth (no NUTRIENT_DWS_API_KEY set): the same browser-flow token used by every other tool also covers Data Extraction. No extra configuration is needed.
• Static API key: Data Extraction is a separate product with its own tenant, so the Processor key in NUTRIENT_DWS_API_KEY cannot be reused. Set NUTRIENT_DWS_EXTRACTION_API_KEY to a Data Extraction key from the dashboard. Without it, parse_document and extract_fields return an error instead of calling the API.

parse_document runs one of four processing modes:

• Spatial output returns typed elements (paragraphs, tables, key-value regions, formulas, pictures, handwriting) with bounding boxes, confidence scores, and reading order. Because the element list can be large, it is written to outputPath and the tool returns a content-free summary (element counts, low-confidence flags, page geometry).
• Markdown output returns whole-document Markdown inline, or writes it to outputPath when provided (recommended for large documents) — useful for RAG and search indexing.
• Both at once: pass formats: ["spatial", "markdown"] instead of format to get output.elements and output.markdown in one call. The second format is billed at no extra cost. outputPath is required (as for spatial alone); the summary also reports the markdown byte length.

The document can be supplied either as filePath (uploaded from the local file system or sandbox) or url (fetched directly by the API) — provide exactly one.

Additional options:

• language — OCR language(s) for structure/understand/agentic modes; left unset, the API auto-detects. maxLanguages / maxScripts cap how many languages/scripts auto-detection considers, and only apply when language is left unset.
• Markdown-only formatting: useHtmlTables (default true), enableSemanticBlockFormatting (default true), includeHeadersAndFooters (default false), extractWordsFromPictures (default false).
• Each response reports Data Extraction credit usage. These are a separate balance from the Processor API credits reported by check_credits.

Note: Extracted content returned inline (Markdown output, or extract_fields results) enters the conversation and may be logged by the host. For sensitive documents, prefer spatial output to a file plus targeted extract_fields calls.

Schema-Guided Extraction (extract_fields)

Where parse_document parses a whole document into elements or Markdown, extract_fields pulls out only the fields you name. Pass a JSON schema (schema) whose root is type: "object" with properties — the response's output.data matches that shape, e.g. { invoiceNumber, total, lineItems: [...] }.

• Supported schema keywords: type, properties, required, items, description, string enum, and format: "date". $ref/$defs and composition/conditional keywords (allOf/anyOf/oneOf/if/then/else) are rejected. Schemas are closed — do not set additionalProperties yourself. Limits: 32 KB serialized, 500 fields, 50 properties per object, 5 nesting levels, enum values capped at 50.
• mode runs the parse feeding the extraction: structure (1.5 credits/page), understand (default, 9 credits/page), or agentic (18 credits/page) — no text mode, since schema-guided extraction needs the structural parse text mode skips. Total cost per page is that parse component plus a fixed extract component, in Data Extraction credits.
• output.data is always returned inline, pretty-printed — it is the answer, bounded by your own schema. Alongside it, a citation match summary reports how each field was grounded (id_match, id_match_multiblock, id_match_partial, fuzzy_match, not_found) and lists which field paths came back not_found (capped at 10, then "+N more"). Field paths come from your schema, not the document, so this leaks no document content.
• Per-field citations (bounding box, confidence, match quality) and page geometry are only kept when outputPath is set — they can be large and add little without the document open alongside them. Without outputPath, a note says they were omitted.
• includeCitations (server default true), strict (default false), and multimodal (default false, increases cost/latency) are only sent when you set them explicitly.
• instructions (free text, up to 10000 characters) adds guidance for ambiguous fields. language/maxLanguages/maxScripts tune OCR the same way as parse_document.

Usage Examples

These examples assume your files live inside the configured sandbox and that you use explicit paths.

Example 1: HTML -> PDF -> signing

User prompt: Convert /path/to/sandbox/invoice.html to PDF and save it as /path/to/sandbox/invoice.pdf. Then digitally sign /path/to/sandbox/invoice.pdf with a visible signature and save it as /path/to/sandbox/invoice-signed.pdf.

What happens: The server uploads the HTML file to Nutrient, saves the generated PDF in the sandbox, then signs that PDF and writes the signed result back to the requested output path.

Example 2: OCR extraction

User prompt: Run OCR on /path/to/sandbox/scanned-contract.pdf, return the extracted text, and save the OCR'd file as /path/to/sandbox/scanned-contract-ocr.pdf.

What happens: The server sends the scanned PDF to Nutrient for OCR, returns the extracted text in Claude, and writes the OCR-processed file back to the sandbox for later use.

Example 3: Check credits -> process -> inspect output

User prompt: Check my Nutrient credits, convert /path/to/sandbox/report.docx to PDF, save it as /path/to/sandbox/report.pdf, and then tell me where the output file was written.

What happens: The server first performs a read-only account lookup, then converts the DOCX file to PDF, saves the result in the sandbox, and tells the user exactly where the output file was written.

Example 4: Schema-guided field extraction with citations

User prompt: Extract vendor_name, invoice_number, total_amount and due_date from /path/to/sandbox/invoice-0341.pdf and save the citations next to it.

What happens: The agent calls extract_fields with a small JSON schema ({ "type": "object", "properties": { "vendor_name": {"type": "string"}, "invoice_number": {"type": "string"}, "total_amount": {"type": "string"}, "due_date": {"type": "string"} } }) and an outputPath. The server sends the PDF to the Data Extraction API, returns the four values inline as JSON with a citation match summary, and writes the full per-field citations (page, bounding box, confidence) to the output file for auditing.

Use with AI Agent Frameworks

This MCP server works with any platform that supports the Model Context Protocol:

• Claude Desktop — Direct MCP integration
• Cursor — AI-powered IDE with MCP support
• Windsurf — AI-powered IDE with MCP support
• VS Code + Copilot — GitHub Copilot MCP integration
• LangChain / LangGraph — Via MCP tool adapters
• OpenAI Agents SDK — Via MCP server integration
• Custom agents — Any MCP-compatible system

Why Nutrient?

The Read-Write Gap

AI can read and understand documents — but most tools stop there. Nutrient gives AI agents the ability to actually manipulate documents: merge, redact, sign, watermark, convert formats, extract structured data, and more.

• Beyond PDF reading — Not just text extraction. Full document creation, editing, and transformation.
• Production-grade — Trusted by thousands of companies for mission-critical document processing.
• Standards-compliant — PAdES digital signatures, PDF/A archiving, PDF/UA accessibility.
• Cloud-native — No infrastructure to manage. Send documents to the API, get results back.
• Comprehensive redaction — Built-in presets for SSNs, credit cards, phone numbers, emails, dates, and more.
• Multi-format — Process PDFs, Office documents, images, HTML, and Markdown.

Configuration

Sandbox Mode (Recommended)

The server supports sandbox mode that restricts file operations to a specific directory. Set the SANDBOX_PATH environment variable to enable it:

export SANDBOX_PATH=/path/to/sandbox/directory
npx @nutrient-sdk/dws-mcp-server

Supported CLI flags are --sandbox <dir> and -s <dir>. Unrecognized flags cause a startup error.

When sandbox mode is enabled:

• Relative paths resolve relative to the sandbox directory
• All input file paths are validated to ensure they reside in the sandbox
• Processed files are saved within the sandbox

Note: If no sandbox directory is specified, the server operates without file path restrictions. Sandbox mode is strongly recommended for security.

Output Location

Processed files are saved to a location determined by the AI. To guide output placement, use explicit output paths such as save the result to /path/to/sandbox/output/result.pdf or create an output directory in your sandbox.

Authentication

The server authenticates to the Nutrient DWS API (https://api.nutrient.io) using one of:

When no API key is configured, the server stays connected and opens a browser-based OAuth flow on the first request that uses the Nutrient API (similar to gh auth login). Tokens are cached at $XDG_CONFIG_HOME/nutrient/credentials.json or ~/.config/nutrient/credentials.json and refreshed automatically.

Data Extraction (parse_document, extract_fields) is a separate product with its own tenant. Under OAuth, one token covers both products — nothing extra to configure. Under a static API key, the Processor key in NUTRIENT_DWS_API_KEY cannot be reused for extraction; set NUTRIENT_DWS_EXTRACTION_API_KEY to a Data Extraction key from the dashboard, or omit NUTRIENT_DWS_API_KEY entirely to use OAuth instead.

Setting only NUTRIENT_DWS_EXTRACTION_API_KEY (with no NUTRIENT_DWS_API_KEY) runs the server in extraction-only mode: parse_document and extract_fields work normally, while the Processor tools (document_processor, document_signer, ai_redactor, check_credits) return an error instead of calling the API.

Environment Variables