MinerU MCP Server
Related Servers
Alternatives to MinerU MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseBqualityDmaintenanceConverts documents (PDF, Word, PPT, images, HTML) to Markdown via MinerU API. Supports URL and local file inputs with OCR, formula/table recognition, and automatic large file handling.55MIT
- FlicenseNot gradedqualityDmaintenanceConverts documents (PDF, DOCX, images, etc.) to Markdown using Microsoft's Markitdown library, with no local setup required. Integrates with AI agents via MCP for seamless document conversion.1-
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to convert Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, PDF and image files into Markdown through a single MCP tool that accepts a URL or base64 content. Scanned and image-only pages are OCR'd locally for English, Japanese and Thai, so nothing leaves the container.MIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to extract text from PDF, image, Word, PowerPoint, and Excel files using MinerU, either on the local machine or a self-hosted private-network API. Parsed results are saved locally as Markdown, structured JSON, and extracted images, which can then be read back in segments.MIT
- AlicenseNot gradedqualityAmaintenanceEnables document conversion and processing through MCP, including Office/PDF/Markdown conversions, OCR, and PDF operations like split, rotate, encrypt, and extract.MIT
- AlicenseAqualityBmaintenanceEnables AI agents and MCP-capable platforms to convert documents such as docx, pptx, xlsx, pdf, odt, rtf, epub, and csv into clean GitHub-Flavored Markdown through a single tool call. Runs fully locally with no API key, no network access, and no OCR, returning structured errors for scanned, encrypted, or unsupported files.110 npmMIT
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: get_ocr_languages retrieves a list of supported languages for OCR, while parse_documents converts files to Markdown format. There is no overlap in functionality, making it easy for an agent to select the appropriate tool without confusion.
Both tools follow a consistent verb_noun naming pattern (get_ocr_languages and parse_documents), using snake_case throughout. This predictability enhances readability and usability for agents, with no deviations in style.
With only 2 tools, the server feels thin for a document processing domain that might include OCR and parsing. While the tools cover specific tasks, the scope suggests potential gaps (e.g., no tools for editing, saving, or managing documents), making it borderline too few for comprehensive functionality.
The tools cover basic OCR language retrieval and document parsing, but there are notable gaps in the document processing lifecycle. For example, there are no tools for creating, updating, or deleting documents, which could limit agent workflows and lead to dead ends in more complex tasks.