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Built with ❤️ by Krishna Goyal

    Pdf Reader Mcp

    An MCP server built with Node.js/TypeScript that allows AI agents to securely read PDF files (local or URL) and extract text, metadata, or page counts.

    294 stars
    TypeScript
    Updated Nov 4, 2025
    ai-agent
    llm-tool
    mcp
    model-content-protocol
    nodejs
    pdf
    pdf-parse
    pdf-parser
    pdf-reader
    stdio
    typescript

    Table of Contents

    • Give your AI agent eyes for PDFs.
    • Why this exists
    • Install (30 seconds)
    • Quick start
    • What you get
    • Flagship use cases
    • Install footprint (honest product comparison)
    • Performance
    • Engine note
    • Product proof
    • Docs
    • License

    Table of Contents

    • Give your AI agent eyes for PDFs.
    • Why this exists
    • Install (30 seconds)
    • Quick start
    • What you get
    • Flagship use cases
    • Install footprint (honest product comparison)
    • Performance
    • Engine note
    • Product proof
    • Docs
    • License

    Documentation

    Why this exists

    Plain text vs evidence

    Most PDF tools dump text. Agents then invent page numbers, miss tables, and cite the wrong cell.

    PDF Reader MCP returns an Agent Document Twin: markdown + structure + geometry + provenance your agent can actually trust.

    Without evidenceWith PDF Reader MCP
    “The revenue was about $12M”“Page 14, Table 3, cell (row 4, col 2) = $12.4M”
    Lost table structureRows, columns, cells, bounding boxes
    Scanned PDF becomes noiseOCR path with page-linked evidence
    Hidden text / prompt injection ignoredTrust signals when requested

    Install (30 seconds)

    bash
    npm install -g @sylphx/pdf-reader-mcp

    Or pin the current release:

    bash
    npm install -g @sylphx/pdf-reader-mcp@4.1.1

    One native binary is installed for your platform only (not all five).

    PlatformNative package (auto optionalDependency)
    macOS arm64@sylphx/pdf-reader-mcp-darwin-arm64
    macOS x64@sylphx/pdf-reader-mcp-darwin-x64
    Linux x64@sylphx/pdf-reader-mcp-linux-x64-gnu
    Linux arm64@sylphx/pdf-reader-mcp-linux-arm64-gnu
    Windows x64@sylphx/pdf-reader-mcp-win32-x64-msvc

    Missing native package → fail closed (no silent engine switch).

    Quick start

    Claude Code

    bash
    claude mcp add pdf-reader -- npx @sylphx/pdf-reader-mcp

    Claude Desktop / Cursor / VS Code / any MCP client

    json
    {
      "mcpServers": {
        "pdf-reader": {
          "command": "npx",
          "args": ["@sylphx/pdf-reader-mcp"]
        }
      }
    }

    Dual-era hosts that send server/discover before initialize (e.g. Gemini Antigravity CLI) are supported on stdio — the server answers discovery and keeps the session open for the legacy handshake.

    Stdio / HTTP

    bash
    pdf-reader-mcp
    MCP_TRANSPORT=http pdf-reader-mcp

    What you get

    Three tools. One product surface.

    ToolWhat agents use it for
    read_pdfSmart default: markdown, tables, structure, OCR, citations
    search_pdfFind page + snippet matches before deep reading
    pdf_evidenceCrops, renders, inspect, focused evidence ops

    Minimal call:

    json
    {
      "sources": [{ "path": "/absolute/path/to/report.pdf" }]
    }

    Flagship use cases

    1. Financial reports — extract table cells agents can cite by page and geometry

    2. Research papers — headings, reading order, page-level quotes

    3. Scanned documents — OCR path with evidence, not a text soup

    Install footprint (honest product comparison)

    Compare full clean installs, not “JS wrapper tarball vs native executable”:

    Metric (measured clean install, linux-x64)Historical TS 3.0.14Sole-Rust 4.1.0
    Main package on disk~403 KB~77 KB
    Full node_modules~82.3 MiB~24.4 MiB (~3.4× smaller)
    Installed files4,10120 (~205× fewer)
    Production npm dependency graphPDF.js + MCP TS SDK + more{} + one platform native

    The native binary is multi-megabyte because it is the PDF intelligence engine (parser, server, rendering/table/OCR routing). That is expected and still yields a cleaner, smaller install than shipping PDF.js + a JS dependency tree.

    Details: installed footprint comparison

    Performance

    Controlled same-host linux-x64 dual-mode A/B vs @sylphx/pdf-reader-mcp@3.0.14, using registry-installed 4.1.x natives:

    ModeWhat it measuresResult
    persistent_warmlong-lived server, repeated identical local read_pdf after warm-up≥ ~10× median latency improvement on all 8 required fixture classes
    startup_inclusivespawn + initialize + one tasklarge advantage on the same fixtures

    persistent_warm includes a process-local cache for identical local path+options. First request in a process still pays full parse cost.

    Also: install footprint is much smaller than TS 3.0.14 on measured linux-x64 (~3.4× less disk, ~205× fewer files), and the 4.1.0 native binary is smaller than 4.0.2 (strip/LTO).

    Not a multi-host guarantee. Details: 4.1.0 report · claims policy

    Engine note

    Version 4 runs a native Rust engine on supported platforms via a thin Node launcher.

    Local-first. Five platforms. One clean install.

    Engineering history, recovery pins, and ADRs live under docs/migration.md — not the product pitch.

    Product proof

    • Before/after + flagship workflows
    • Example demos

    Docs

    • Website / guide
    • Installation
    • Comparison
    • Migration / recovery (secondary)

    License

    MIT

    ---

    If this saves your agents from PDF hallucinations, star the repo and share a demo with your team.

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