Pdffigures2 MCP Server

The pdffigures2-MCP-Server is a specialized Model Context Protocol (MCP) endpoint that brings the powerful figure‑and‑table extraction capabilities of pdffigures2 into AI‑driven workflows. Scholarly PDFs are notoriously dense, with figures and tables interwoven among text and captions that vary across disciplines. This server tackles the challenge of reliably locating, classifying, and extracting those visual elements so that an AI assistant can reference them directly or transform the data into structured formats.

When a PDF is sent to the server, it parses the document’s layout and metadata to identify every figure, table, caption, and section heading. The output is a JSON payload that lists each visual element’s bounding box coordinates, extracted caption text, and an optional image or table data representation. Developers can then feed this structured information into downstream processes—such as summarization, citation generation, or content enrichment—without the need for manual annotation. The server’s integration with MCP means it can be called by any AI assistant that understands the protocol, making it a plug‑and‑play component in larger research or publishing pipelines.

Key capabilities include:

• Accurate layout detection that works across diverse journal formats and languages.
• Caption extraction with optional language detection, enabling multilingual support.
• Section title recognition, allowing contextual grouping of figures and tables within the document’s hierarchy.
• Export options for image thumbnails or CSV‑like table data, facilitating quick visual inspection or data ingestion.

Typical use cases span academic research assistants that need to pull figures into literature reviews, automated publishing platforms that must tag and index visual content, and data‑science pipelines that convert tables into structured datasets for analysis. By exposing these functions through MCP, the server fits seamlessly into any AI workflow that already communicates via this protocol—whether it’s a Claude chatbot, a custom GPT‑based assistant, or an internal tooling suite.

What sets this MCP server apart is its focus on scholarly PDFs, a domain where figure extraction is especially error‑prone due to complex formatting. The combination of precise detection, multilingual caption handling, and MCP compatibility gives developers a robust, ready‑to‑use tool that reduces the friction of integrating visual data extraction into AI applications.