#118 · Primary category: Knowledge Base & RAG

pdf-reader-mcp

agent-document-twin ai-agent ai-tools citations document-intelligence document-processing evidence-first llm-tool mcp model-context-protocol ocr pdf pdf-intelligence pdf-reader pdf-to-markdown rag rust trust-report visual-evidence

Give your AI agent eyes for PDFs — structured text, tables, OCR, visual evidence, and page-level citations via MCP. Native Rust, local-first.

Project last updated:08/30/26

GitHub Stars

906

Forks

81

Contributors

12

License

MIT

Why we included this project

RAG pipelines and document-grounded agents often stumble on PDFs because plain-text extraction flattens layout and leaves claims unverifiable. Citra, shipped as the @sylphx/citra MCP server, fixes that by returning a structured document model with rows, columns, cell bounding boxes, OCR for scanned pages, and page-level location metadata an agent can cite back to a human. That matters for teams working with financial reports, research papers, or legal documents, where an answer that comes with page and cell coordinates beats a number the model invented. Setup is one npx command with no Docker or API key, and since OCR and extraction run in a native Rust engine, PDFs stay on the local machine. The server also fails closed if the native binary is missing, so production output won't degrade silently.

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