ocrmypdf-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ocrmypdf-mcpOCR the scanned PDF at ~/Downloads/scan.pdf"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ocrmypdf-mcp
A minimal MCP server that exposes ocrmypdf as a single tool, ocr_pdf, so Claude can OCR scanned PDFs and then hand them to markitdown (or any text tool) for downstream work.
Why this exists: the obvious "just call ocrmypdf" approach falls over on Windows with the Microsoft Store (MSIX) build of Claude Desktop, because MSIX launches MCP servers with a stripped-down PATH that doesn't include Tesseract or Ghostscript. This server auto-detects the standard Windows install locations and prepends them to PATH at startup, so OCR Just Works without futzing with system environment variables.
Works on Linux and macOS too — the PATH augmentation is a no-op outside Windows.
Prerequisites (Windows)
Two system installers, then pip install.
1. Tesseract OCR
UB-Mannheim build (the standard Windows distribution): https://github.com/UB-Mannheim/tesseract/wiki
Accept the default install location (C:\Program Files\Tesseract-OCR). Add language packs during install if you need anything beyond English.
2. Ghostscript
AGPL release for Windows (free): https://www.ghostscript.com/releases/gsdnld.html
Accept the default install location (C:\Program Files\gs\gs<version>\).
3. Verify (optional)
tesseract --version
gswin64c --versionIf either says "not recognized," reopen PowerShell so it picks up the updated PATH, then retry.
Related MCP server: pdf-filler-recursive-simple-mcp
Install the server
git clone https://github.com/jcm4TX/ocrmypdf-mcp
cd ocrmypdf-mcp
pip install --user .This installs ocrmypdf, the mcp SDK, and the ocrmypdf-mcp executable. On Windows it lands at:
C:\Users\<you>\AppData\Roaming\Python\Python313\Scripts\ocrmypdf-mcp.exeWire it up in Claude Desktop
Edit claude_desktop_config.json. On the MSIX (Microsoft Store) build of Claude Desktop, the path is:
%LOCALAPPDATA%\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\claude_desktop_config.jsonOn the regular non-MSIX installer it's:
%APPDATA%\Claude\claude_desktop_config.jsonAdd an ocrmypdf-mcp entry under mcpServers:
{
"mcpServers": {
"ocrmypdf-mcp": {
"command": "C:\\Users\\<you>\\AppData\\Roaming\\Python\\Python313\\Scripts\\ocrmypdf-mcp.exe",
"args": []
}
}
}Then fully quit Claude Desktop — right-click the tray icon and pick Quit, not just close the window — and relaunch.
Verify it loaded
In a new chat, ask "what MCP tools do you have for OCR?" — Claude should report ocr_pdf. If not, check the server log:
%LOCALAPPDATA%\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\logs\mcp-server-ocrmypdf-mcp.logTool API
ocr_pdf(input_path, output_path?, language?, force_ocr?, deskew?)
Arg | Type | Default | Meaning |
| str | required | Absolute path to input PDF |
| str |
| Where to write the OCR'd PDF |
| str |
| Tesseract language code; join multiple with |
| bool |
| Re-OCR pages that already have a text layer |
| bool |
| Straighten skewed pages before OCR |
Default behavior: pages without an existing text layer get OCR'd, pages that already have text pass through unchanged. Safe to run on mixed PDFs.
Typical workflow
You hand Claude a scanned PDF path.
Claude calls
ocr_pdf(input_path="...").Claude calls
markitdown.convert_to_markdownon the resulting-ocr.pdf.Claude reads the markdown and answers your question.
Known limitations
The MCP protocol enforces a per-request timeout (~4 minutes in current Claude Desktop). Large multi-page documents may exceed this and surface as a client-side timeout even though the underlying
ocrmypdfprocess completes successfully — the output PDF will still be on disk. If you hit this regularly, split the input into smaller page ranges first.Complex multi-column scanned layouts (legal, probate, ledgers) can produce messy markdown when piped to markitdown afterward, because Tesseract interprets visual alignment as table structure. Post-processing the markdown to drop empty table-pipe rows recovers most of it.
License
Available Tools
1 toolocr_pdfA
Run OCR on a PDF to add a searchable text layer using Tesseract.
Use this when a PDF is scanned (no extractable text). After running, the output PDF has an invisible text layer and can be converted to markdown via markitdown, searched in Adobe / Windows Search, or copied from normally.
Args: input_path: Absolute path to the input PDF on the user's filesystem. output_path: Absolute path for the OCR'd output PDF. If omitted, defaults to "-ocr.pdf" in the same directory as input. language: Tesseract language code (e.g. "eng", "eng+spa", "deu"). Multiple languages are joined with "+". Default "eng". force_ocr: If True, OCR every page even if a text layer already exists. Use when the existing text layer is poor or junk OCR. Default False (pages with existing text are passed through unchanged). deskew: If True, straighten skewed pages before OCR. Default True.
Returns:
A dict with success (bool), output_path (str on success), and
error (str on failure).
| Name | Required | Description | Default |
|---|---|---|---|
| input_path | Yes | ||
| output_path | No | ||
| language | No | eng | |
| force_ocr | No | ||
| deskew | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It explains the invisible text layer, return values, and parameter behaviors (e.g., force_ocr for existing layers, deskew). Lacks details about file overwriting or performance, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: purpose, usage, args, returns. Concise but not overly brief. A few phrases could be trimmed, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 params, no output schema), the description is complete. It explains the tool's function, when to use, all parameters with defaults, and the return format. An agent can confidently invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must explain all parameters. It provides detailed Args section explaining each parameter's purpose, defaults, and nuances (e.g., 'force_ocr: Use when the existing text layer is poor or junk OCR'). Adds significant value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action: 'Run OCR on a PDF to add a searchable text layer using Tesseract.' It specifies the verb (Run OCR), resource (PDF), and outcome. Even without sibling tools, it makes the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Use this when a PDF is scanned (no extractable text).' It also explains capabilities of output (searchable, convertible). Does not explicitly mention when not to use, but the condition is clear. Could mention alternatives, but none are listed as siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.0- First observed
ocr_pdf
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion or overlap with other tools.
With a single tool, naming is inherently consistent; 'ocr_pdf' clearly conveys the action and target.
A single tool for OCR seems thin; typically one might expect separate tools for checking text presence or converting formats, but the comprehensive parameter set partially justifies the minimal count.
The tool covers core OCR functionality with useful options (language, deskew, force_ocr), but lacks a companion tool to inspect existing text layers, a minor gap.
Maintenance
Related MCP Connectors
PDF tools for Claude: merge, split, compress, convert, OCR & more. Requires a PDFHaul API key.
Extract tables, text and formulas from PDFs, including scanned pages and broken text layers.
Generate PDFs from templates via AI chat. Works with Claude, ChatGPT, Cursor, and any MCP client.
High-fidelity PDF to structured Markdown conversion and document field extraction.
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