Parse PDFs, office documents, and images through MinerU with OCR, batch jobs, page ranges, and local file uploads.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
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π‘ 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 Mineru MCP.
mineru_parseParse a document URL
mineru_statusCheck task progress, get download URL
mineru_batchParse multiple URLs (max 200)
mineru_batch_statusGet batch results with pagination
mineru_upload_batchUpload local files for batch parsing
mineru_download_resultsDownload results as named markdown files
The linxule/mineru-mcp MCP server exposes MinerUβs document parsing API as MCP tools. It is designed for extracting text, tables, and formulas from PDFs, office documents, presentations, and image files. Supported inputs include PDF, DOC, DOCX, PPT, PPTX, PNG, JPG, and JPEG files.
Agents can submit a document URL with mineru_parse, process several URLs with mineru_batch, or upload local files through mineru_upload_batch. Parsing options include the VLM or pipeline model, page ranges, OCR, formula recognition, table recognition, language selection, and additional export formats such as HTML. OCR support covers 109 languages.
MinerU jobs run asynchronously. A single URL submission returns a task that can be tracked with mineru_status; the status response can include a download URL for the resulting archive. Batch submissions return a batch identifier, which can be checked with mineru_batch_status. Batch status supports pagination through limit and offset values and can return concise or detailed output.
For local files, the typical sequence is upload, poll for completion, and download. mineru_upload_batch can scan a directory or accept an explicit list of files. Once processing finishes, mineru_download_results writes the parsed output as Markdown files. Output names are derived from each fileβs data identifier, with spaces converted to underscores, and the tool can optionally overwrite existing files.
The server selects the pipeline model by default. The VLM model is available for complex documents, while the pipeline model is intended for faster processing of simpler documents. OCR can be enabled for pipeline processing, and page ranges can limit extraction to selected pages.
The linxule/mineru-mcp MCP server requires Node.js 18 or later and a MinerU API key. Install it as a local stdio server with npx -y mineru-mcp, then provide the key through MINERU_API_KEY.
Configuration variables are:
MINERU_API_KEY: required MinerU bearer token.MINERU_BASE_URL: optional API base URL; defaults to https://mineru.net/api/v4.MINERU_DEFAULT_MODEL: optional default model, either pipeline or vlm; defaults to pipeline.The README provides configuration examples for Claude Desktop, Cursor, Windsurf, Cline, VS Code, and several CLI clients. ChatGPT does not directly support this local stdio setup; it requires a public HTTPS deployment using HTTP transport.
The linxule/mineru-mcp MCP server provides these tools:
mineru_parse: submit one document URL for parsing.mineru_status: inspect a task and obtain its result download URL.mineru_batch: submit up to 200 document URLs in one request.mineru_batch_status: retrieve batch results with pagination.mineru_upload_batch: upload local files from a directory or file list.mineru_download_results: save batch results as named Markdown files.Single files are limited to 200 MB and 600 pages. The service documentation also lists a daily high-priority quota of 2,000 pages. Batch requests can contain at most 200 files.
This server depends on the MinerU API and therefore requires a valid MinerU bearer token and network access to the configured API endpoint. It does not parse files independently on the local machine. Results from URL and batch operations must be tracked before they can be downloaded.
Local upload paths and output directories must be supplied by the caller. The README uses placeholders for these paths, so they need to be replaced with paths available to the machine running the MCP client. The project is licensed under MIT.
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