Pdf2md

Created By
FutureUnreala year ago
PDF to Markdown conversion tool
Overview

what is Pdf2md?

Pdf2md is a high-performance service that converts PDF files into structured Markdown format, utilizing the MinerU API for efficient processing.

how to use Pdf2md?

To use Pdf2md, clone the repository, set up a virtual environment, install dependencies, configure environment variables, and start the service using command line arguments.

key features of Pdf2md?

  • Format Conversion: Converts PDF files to Markdown format.
  • Multiple Sources: Supports local files and URL links.
  • Intelligent Processing: Automatically selects the best processing method.
  • Batch Processing: Allows for efficient conversion of multiple files.
  • OCR Support: Optional OCR to enhance recognition rates.
  • MCP Integration: Works seamlessly with LLM clients like Claude Desktop.

use cases of Pdf2md?

  1. Converting academic papers from PDF to Markdown for easier editing.
  2. Batch processing of multiple PDF documents for documentation purposes.
  3. Integrating with other tools for automated content extraction and formatting.

FAQ from Pdf2md?

  • What is required to run Pdf2md?

You need Python 3.10+ and an API key from MinerU.

  • Can Pdf2md process multiple files at once?

Yes! It supports batch processing for efficient handling of large volumes of PDFs.

  • Is there any cost associated with using Pdf2md?

The service is free to use, but you need to obtain an API key from MinerU, which may have its own terms.

Server Config

{
  "mcpServers": {
    "pdf2md": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\path\\to\\mcp-pdf2md",
        "run",
        "pdf2md",
        "--output-dir",
        "C:\\path\\to\\output"
      ],
      "env": {
        "MINERU_API_KEY": "your_api_key_here"
      }
    }
  }
}
Project Info
Created At
a year ago
Updated At
a year ago
Author Name
FutureUnreal
Star
-
Language
-
License
-

Recommend Servers

View All
Tavily Mcp
@tavily-ai

JavaScript
a year ago
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago