Process, extract, redact, sign, and transform PDFs and Office documents through Nutrient DWS using natural language.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ or use 1-click editor setup below.
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ we're steadily working through the catalog.
๐ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Nutrient Dws MCP Server.
document_processorDocument processing for conversions, OCR, watermarking, rotation, annotation flattening, and redaction workflows
parse_documentStructured data extraction (DWS Data Extraction API): typed JSON elements with bounding boxes and confidence, or whole-document Markdown
extract_fieldsSchema-guided field extraction (DWS Data Extraction API): pulls specific named fields into a JSON shape you define, with per-field citations
document_signerPDF signing with CMS / PKCS#7 and CAdES signatures plus visible or invisible appearance options
ai_redactorAI redaction for detecting and permanently removing sensitive content such as names, addresses, SSNs, emails, and custom criteria
check_creditsRead-only account lookup for current DWS credits and usage. No document content is uploaded

Give AI agents the power to generate, read, extract, process, and sign documents.
A Model Context Protocol (MCP) server that connects AI assistants to the Nutrient Document Web Service (DWS) Processor and Data Extraction APIs โ 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.
text (1 credit/page, no OCR), structure (1.5), understand (9, the default), agentic (18, VLM)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..."
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.
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.
Choose your platform and add the configuration:
Open Settings โ Developer โ Edit Config, then add:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Create .cursor/mcp.json in your project root:
Add to ~/.codeium/windsurf/mcp_config.json:
Create .vscode/mcp.json in your project, or add the same server definition to your user mcp.json profile:
Any MCP-compatible client can connect using stdio transport:
Restart the application to pick up the new MCP server configuration.
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.
| Tool | Description |
|---|---|
document_processor | Document processing for conversions, OCR, watermarking, rotation, annotation flattening, and redaction workflows |
parse_document | Structured data extraction (DWS Data Extraction API): typed JSON elements with bounding boxes and confidence, or whole-document Markdown |
extract_fields | Schema-guided field extraction (DWS Data Extraction API): pulls specific named fields into a JSON shape you define, with per-field citations |
document_signer | PDF signing with CMS / PKCS#7 and CAdES signatures plus visible or invisible appearance options |
ai_redactor | AI redaction for detecting and permanently removing sensitive content such as names, addresses, SSNs, emails, and custom criteria |
check_credits | Read-only account lookup for current DWS credits and usage. No document content is uploaded |
sandbox_file_tree | Read-only view of files inside the configured sandbox directory |
directory_tree | Read-only view of local files when sandbox mode is disabled. Sandbox mode is strongly recommended |
sign_and_watermark โ Add a text watermark to a document, then digitally sign the watermarked PDF.extract_document_fields โ Extract named fields into a JSON object, optionally retaining citations in a file.redact_pii โ Detect and permanently redact personally identifiable information from a document.parse_for_rag โ Parse a document as Markdown for retrieval-augmented generation and search indexing.office_to_pdfa โ Convert an Office document to an archival PDF/A file.Factual signals from GitHub, npm, and our automated checks โ not a rating.
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