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Glama

PDF, Word, PowerPoint, Excel, EPUB, HTML and web pages, images (OCR), audio and video (metadata, subtitles, transcripts). A fast Rust MCP server and CLI that runs on your machine. No API key.

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Install · Benchmarks · Tools · CLI · Formats · Docs

Formerly pdf-reader-mcp. Migrating from pdf-reader-mcp

A real, unedited terminal recording (asciinema + agg, script). The last command is Claude Code answering from the PDF through the anymd MCP server.

Why anymd

  • Fast. Native Rust converts in parallel, page by page. On the 19 benchmark documents every tool converted, anymd takes 12.1 s in total; docling 1,723.3 s (143×), markitdown 45.4 s (4×), marker 5,256.3 s (435×).

  • Accurate. A layout engine rebuilds words from glyph gaps, puts two-column papers in reading order, and recovers tables, including borderless ones. The text stays exactly as printed, with no glued words and no scrambled columns.

  • Lean on tokens. Pages come back as Markdown with <!-- page 3 --> citation anchors, a small front-matter header, and compact tables. A token budget and a cursor keep large documents within your agent's context.

  • Every format, one call. One tool reads every format listed below. It also accepts web URLs and whole directories, and search looks across all of them.

  • Local and private. Nothing is uploaded. OCR and transcripts use local tools you already have (tesseract, ffmpeg, whisper.cpp), and only when they are installed.

Related MCP server: pdfeverything

Install

Add anymd to every MCP client on your machine (Claude Code, Codex, Cursor, VS Code, Claude Desktop, Windsurf, Gemini CLI) with one command:

npx -y @sylphx/anymd setup     # --dry-run to preview, --remove to undo

Or add it by hand: every MCP client runs the same command, npx -y @sylphx/anymd. Node 18+ is the only requirement; npm installs the native binary for your platform.

claude mcp add anymd -- npx -y @sylphx/anymd

Or as a plugin, with the anymd skill: /plugin marketplace add SylphxAI/anymd, then /plugin install anymd@anymd.

codex mcp add anymd -- npx -y @sylphx/anymd

or in ~/.codex/config.toml:

[mcp_servers.anymd]
command = "npx"
args = ["-y", "@sylphx/anymd"]

Add to Cursor

or in .cursor/mcp.json:

{ "mcpServers": { "anymd": { "command": "npx", "args": ["-y", "@sylphx/anymd"] } } }
code --add-mcp '{"name":"anymd","command":"npx","args":["-y","@sylphx/anymd"]}'

or in .vscode/mcp.json:

{ "servers": { "anymd": { "type": "stdio", "command": "npx", "args": ["-y", "@sylphx/anymd"] } } }

Add to claude_desktop_config.json (Settings → Developer → Edit Config):

{ "mcpServers": { "anymd": { "command": "npx", "args": ["-y", "@sylphx/anymd"] } } }

Any client that speaks MCP over stdio: command npx, args ["-y", "@sylphx/anymd"]. To keep the server inside one folder, add --allow-dir=/path/to/docs.

npm install -g @sylphx/anymd     # or run it once with: npx -y @sylphx/anymd <file>

Benchmarks

AgentDocBench is an open benchmark for document → Markdown conversion for agents: license-clean documents in 12 categories (math papers, two-column papers, financial tables, forms, scans, CJK, slides, spreadsheets, Word, EPUB, HTML), scored on verbatim sentences, text F1, reading order, and table cells, with time and output tokens. Every tool runs on the same kind of GitHub-hosted runner (4 CPUs):

anymd

docling

kreuzberg

unstructured

markitdown

marker

pdftotext

Overall score

96.3

93.0

81.7

81.2

76.8

71.0

42.2

Table cells F1

92.2

89.9

38.4

38.4

57.2

60.9

0.0

Reading order

98.8

94.4

96.8

93.9

85.5

76.8

52.0

Docs converted

38/38

38/38

38/38

38/38

38/38

30/38

23/38

Time, all docs

22.9 s

2,432.4 s

16.0 s

346.5 s

75.6 s

7,104.5 s

0.90 s

The generated leaderboard, per-category scores (including where anymd loses), and method are in the benchmark guide. The corpus, ground truth, adapters, and raw results are in bench/, and the Benchmark workflow reruns everything; new tools can join with a single adapter file.

MCP tools

anymd exposes three tools.

Tool

Use it to

Key arguments

read

Turn a file, URL, or folder into Markdown

source, pages ("1-5,8"), max_tokens (default 20000), cursor, ocr, transcript, download_whisper_model

search

Find text across files, folders, and URLs

query, sources, mode (auto · literal · ranked), glob, max_results

inspect

Go deeper on a PDF

operation: render_page, extract_regions, ocr_pages, structure (JSON with geometry), compare, inspect

A read answer looks like this:

---
source: papers/attention.pdf
title: Attention Is All You Need
pages: 15
showing: pages 1-9
---

<!-- page 1 -->

# Attention Is All You Need
…

<!-- page 8 -->

|Model|BLEU EN-DE|BLEU EN-FR|
|-|-|-|
|Transformer (big)|28.4|41.8|
…

<!-- Stopped at the 20000-token budget. Continue with cursor: "10", or pick pages, or raise max_tokens. -->

search answers with one line per hit:

5 matches for "masked language model" (2 files, 31 sections searched)

### papers/bert.pdf (5)
- p.1: …by using a “**masked language model**” (MLM) pre-training objective, inspired by the Cloze task…
- p.2: …In addition to the **masked language model**, we also use a “next sentence prediction” task…

If nothing matches exactly, search falls back to BM25-ranked passages, so a question like "how does bidirectional pretraining work" still finds the right page.

CLI

The same binary is a command-line converter, like MarkItDown but much faster:

anymd report.pdf > report.md                 # a file
anymd deck.pptx notes.docx budget.xlsx        # several files, each with a header
anymd https://example.com/article            # a web page (main content only)
cat scan.png | anymd - --ocr                 # stdin, with OCR
anymd paper.pdf --pages 1-3 --max-tokens 4000
anymd search "indemnification" contracts/ --glob '*.pdf'
anymd doctor                                 # lists the optional tools anymd found

Run with no arguments from an MCP client (piped stdin), or as anymd mcp, and it serves MCP over stdio.

Formats

Input

What you get

PDF

Reading-order Markdown: headings, paragraphs, lists, tables, sub/superscripts, <!-- page N --> markers, bookmarks as an outline. Running headers and page numbers are removed. Image-only pages are OCR'd when tesseract is installed.

Word .docx

Headings, bold/italic, links, nested lists, tables with merged cells, footnotes, equations as LaTeX

PowerPoint .pptx

One section per slide in deck order, titles, bullets, tables, chart data, speaker notes

Excel .xlsx .xls .ods · CSV/TSV

One table per sheet, dates as ISO strings, capped at 2,000 rows per sheet

EPUB

One section per chapter in spine order, plus title and author

HTML and URLs

The main article only: navigation, cookie banners, and sidebars are dropped. Relative links are resolved, and code keeps its language.

Markdown, text, JSON

Returned unchanged, with pagination

Images

Dimensions and EXIF (camera, date, GPS), plus OCR text when tesseract is installed

Audio / video

Duration, streams, chapters, embedded and sidecar subtitles (via ffprobe/ffmpeg). Local whisper.cpp transcript with transcript: true; download_whisper_model: true fetches a verified model on first use.

How it works

For PDFs, anymd reads glyph positions rather than text runs. Glyphs are grouped into lines by baseline, which tolerates super- and subscripts. Word spaces come from the gaps between glyphs, measured against the font size and adjusted for letter tracking. A column-aware XY cut finds gutters between running text. Tables come from drawn lines where a table has them (a missing line between two cells makes a merged cell) and from aligned columns of whitespace where it does not. Wrapped cell text stays in its cell, stacked header lines become one header, and a header over several columns is kept with each of them. Text a reader cannot see (invisible text, or text in the colour of the box behind it) is left out. Pages are processed in parallel and isolated from each other, so one malformed page never fails the whole document. The other formats are parsed natively in Rust (zip/XML, calamine, html5ever); no Python, LibreOffice, or cloud service is involved.

Security

  • Local-first: documents never leave your machine unless you pass a URL, and even then only that URL is fetched.

  • URL fetches block private and loopback addresses, and every redirect hop is checked again, pinned to its resolved address.

  • --allow-dir=<path> (repeatable) or MCP_PDF_ALLOWED_DIRS confines the server to the directories you list.

  • External tools (tesseract, ffprobe, whisper.cpp) are optional. anymd runs them without a shell, with a timeout and an output cap.

See SECURITY.md to report a vulnerability.

Also from Sylphx

  • repomap: A map of your codebase for AI agents: code graph, search, call paths and change impact — with an interactive graph UI. Rust MCP server + CLI. Local, no API key, MIT.

  • lockdocs: Exact-version library docs from your lockfile — local, offline, no rate limits.

  • readme-mark: Beautiful README images from one URL — animated banners, shields-compatible badges, typing text, 3000+ tech icons, GitHub stats cards. Free, no token, drop-in for shields / capsule-render / skill-icons / readme-typing-svg / github-readme-stats.

Star history

Star History Chart

License

MIT © Sylphx

Available Tools

1 tool
read_pdfB

Reads content/metadata from one or more PDFs (local/URL). Each source can specify pages to extract.

ParametersJSON Schema
NameRequiredDescriptionDefault
include_full_textNoInclude the full text content of each PDF (only if 'pages' is not specified for that source).
include_metadataNoInclude metadata and info objects for each PDF.
include_page_countNoInclude the total number of pages for each PDF.
sourcesYesAn array of PDF sources to process, each can optionally specify pages.

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states the tool reads content/metadata and allows page specification, but lacks details on permissions, rate limits, error handling, or output format. For a tool with no annotations, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise and front-loaded: two sentences that efficiently convey core functionality without waste. Every sentence earns its place by stating the main purpose and a key feature.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and 4 parameters with full schema coverage, the description is minimally adequate. It covers the basic action and a feature, but lacks context on behavioral traits, output, or error handling, making it incomplete for a tool with no structured support.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds minimal value beyond the schema by mentioning 'pages to extract,' which aligns with the 'pages' parameter but doesn't provide additional semantics. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Reads content/metadata from one or more PDFs (local/URL).' It specifies the action (reads), resource (PDFs), and scope (content/metadata, multiple sources). However, it doesn't differentiate from siblings since none exist, so it can't achieve a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal usage guidance. It mentions 'Each source can specify pages to extract,' which hints at when to use page specification, but offers no explicit when/when-not scenarios, prerequisites, or alternatives. With no sibling tools, this is less critical, but the guidance remains basic.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedread_pdf

TDQS

B3.2/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'read_pdf' has a clear and distinct purpose focused on extracting content and metadata from PDFs.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'read_pdf' follows a clear verb_noun pattern, which would be consistent if more tools were added.

Tool Count2/5

A single tool is too few for a server named 'PDF Reader MCP Server', as this suggests a broader domain that might include operations like search, annotate, convert, or edit PDFs. The scope feels incomplete with just reading functionality.

Completeness2/5

The tool set is severely incomplete for a PDF reader domain. While 'read_pdf' covers extraction, there are obvious gaps such as searching within PDFs, manipulating pages, adding annotations, converting formats, or handling PDF metadata updates, which are common in such applications.

Maintenance

ActivityActive
ResponsivenessWithin a week

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