Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Read, search and fill PDFs as markdown, text, tables, forms. Your documents stay on your machine.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
PDFLUENT_MCP_MAX_PAGES, PDFLUENT_MCP_MAX_BYTES, PDFLUENT_MCP_MAX_OUTPUT_CHARSDu brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"pdfluent": {
"command": "uvx",
"args": [
"pdfluent-mcp",
"--root"
],
"env": {
"PDFLUENT_MCP_MAX_PAGES": "your-value",
"PDFLUENT_MCP_MAX_BYTES": "your-value",
"PDFLUENT_MCP_MAX_OUTPUT_CHARS": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add pdfluent --env PDFLUENT_MCP_MAX_PAGES=your-value --env PDFLUENT_MCP_MAX_BYTES=your-value --env PDFLUENT_MCP_MAX_OUTPUT_CHARS=your-value -- uvx pdfluent-mcp --rootTrag die leeren Werte ein, bevor du ihn startest.
Dieser Server braucht Werte in seiner Befehlszeile, also kopier die Einstellungen und trag sie ein.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"pdfluent": {
"command": "uvx",
"args": [
"pdfluent-mcp",
"--root"
],
"env": {
"PDFLUENT_MCP_MAX_PAGES": "your-value",
"PDFLUENT_MCP_MAX_BYTES": "your-value",
"PDFLUENT_MCP_MAX_OUTPUT_CHARS": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server braucht Werte in seiner Befehlszeile, also kopier die Einstellungen und trag sie ein.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"pdfluent": {
"type": "stdio",
"command": "uvx",
"args": [
"pdfluent-mcp",
"--root"
],
"env": {
"PDFLUENT_MCP_MAX_PAGES": "your-value",
"PDFLUENT_MCP_MAX_BYTES": "your-value",
"PDFLUENT_MCP_MAX_OUTPUT_CHARS": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
PDFLUENT_MCP_MAX_PAGES envPDFLUENT_MCP_MAX_BYTES envPDFLUENT_MCP_MAX_OUTPUT_CHARS envDiese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto pdfluent bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.3.0) kam vor 3 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
pdfluent-mcp
An MCP server that lets your AI client read, search and fill local PDFs.
An MCP server for PDFluent. It lets Claude Desktop, Claude Code, Cursor, VS Code and any other MCP client read a local PDF as markdown, plain text, tables or positioned blocks, look up its metadata, search it, and fill its form fields into a new file.
Runs on your machine, no upload. It runs over stdio, one process per client, on the pdfluent Python package. Your documents stay on your machine. The server reads and writes only inside the directories you allow. Your documents are never uploaded.
uvx pdfluent-mcp setup --root ~/Documents
This registers a stdio server named pdfluent in the user configs of Claude Desktop, Claude Code, Cursor and Codex, then tells you to restart the clients. pipx run pdfluent-mcp setup works the same way. Without --root, file access relies on client MCP roots or PDFLUENT_MCP_ROOTS (SETUP-001, SETUP-002).
Needs Python 3.10 or later and uv. uvx downloads the server and pdfluent on first use and caches them. The balanced quality tier needs model files; pdfluent models install downloads them once.
| Tool | What it does | Example prompt |
|---|---|---|
extract_markdown | The document as markdown in reading order: headings, list items and tables. Running heads and page numbers are left out. | "Summarise ~/Documents/report.pdf." |
extract_text | The plain text layer, page by page, each page behind . | "Give me the raw text of pages 0 to 2 of contract.pdf." |
extract_tables | One entry per table with page, rows and header_rows. | "Pull the tables out of invoice.pdf." |
extract_blocks | Positioned blocks with kind, text, page, bbox and confidence. Also lists the pages whose text layer is missing. | "List every heading in manual.pdf with its page." |
get_metadata | Page count, title, author, dates, PDF version, encryption, form fields, signatures, bookmarks, and pages with no text layer. | "How many pages does scan.pdf have, and is it encrypted?" |
search | The pages that mention a query, with up to three snippets per page. | "Which pages of policy.pdf mention retention?" |
list_form_fields | Every form field with name, type, value and page. | "Which fields does application.pdf have?" |
fill_form | Fills the named fields and writes a new file. The input is never changed. | "Fill application.pdf with my name and address, save as application-filled.pdf." |
vision_advice | One entry per page with needs_vision and its reasons. | "Which pages of scan.pdf need a vision model?" |
Page numbers are 0-based in every argument and result.
Tool arguments and result details
| Tool | Returns |
|---|---|
extract_markdown(path, pages?, quality?) | The document as markdown in reading order: headings, list items and tables. Running heads and page numbers are left out. quality is fast (default) or balanced. |
extract_text(path, pages?) | The plain text layer, page by page, each page behind . |
extract_tables(path, pages?) | tables: one entry per table with page, rows (lists of cell strings) and header_rows. |
extract_blocks(path, pages?, include_furniture?) | blocks: each with kind, text, page, bbox and confidence, plus level, ordinal or table-cell position where they apply. pages_needing_vision: the pages whose text layer is missing. |
get_metadata(path) | Page count, title, author, dates, PDF version, encryption, whether the document has form fields, how many signatures and bookmarks it has, and which pages have no text layer. |
search(path, query) | The pages that mention query, with up to three snippets per page. |
list_form_fields(path) | Every form field with name, type, value and page. |
fill_form(path, values, out_path, overwrite?) | Fills the named fields and writes a new file at out_path. The input is never changed; an existing out_path is refused unless overwrite is true, and a symbolic link there is always refused. |
vision_advice(path) | advice: one entry per page with needs_vision and its reasons. |
confidence is the probability of the learned model that decided a block's kind; it is null where no model decided it, and null means unknown, not low.
Replace /path/to/documents with the directory your PDFs live in.
Add to claude_desktop_config.json and restart Claude Desktop:
{
"mcpServers": { "pdfluent": { "command": "uvx", "args": ["pdfluent-mcp", "--root", "/path/to/documents"] } } }
Config file location and troubleshooting
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json, Windows: %APPDATA%\Claude\claude_desktop_config.json.
If Claude Desktop does not find uvx, write its full path (which uvx) in command.
claude mcp add pdfluent -- uvx pdfluent-mcp --root /path/to/documents
Project setup for a team, and the Claude skill
Claude Code also offers its working directory as an MCP root, so inside a project claude mcp add pdfluent -- uvx pdfluent-mcp is enough. For a team, put this in the project's .mcp.json:
{
"mcpServers": { "pdfluent": { "type": "stdio", "command": "uvx", "args": ["pdfluent-mcp"] } } }
To tell Claude when to reach for these tools, copy skills/pdfluent from this repository to ~/.claude/skills/pdfluent (every project) or to .claude/skills/pdfluent in one project.
Add to Cursor (then change /path/to/documents), or add to ~/.cursor/mcp.json or the project's .cursor/mcp.json:
{
"mcpServers": { "pdfluent": { "type": "stdio", "command": "uvx", "args": ["pdfluent-mcp", "--root", "/path/to/documents"] } } }
Install in VS Code, or from a terminal:
code --add-mcp '{"name":"pdfluent","type":"stdio","command":"uvx","args":["pdfluent-mcp","--root","/path/to/documents"]}'
Workspace config
Or add to .vscode/mcp.json in the workspace (note the key is servers):
{
"servers": { "pdfluent": { "type": "stdio", "command": "uvx", "args": ["pdfluent-mcp", "--root", "/path/to/documents"] } } }
Add to ~/.codex/config.toml:
[mcp_servers.pdfluent]
command = "uvx" args = ["pdfluent-mcp", "--root", "/path/to/documents"]
One-click install for Cursor and VS Code registers the server with a placeholder root that you then change.
Setup command: flags, backups, uninstall
--root DIR (repeatable) is added to the saved command for each directory you name, for example uvx pdfluent-mcp setup --root ~/Documents.--dry-run shows what would change and writes nothing. .pdfluent-backup- and prints the path.--client claude-desktop|claude-code|cursor|codex limits it to some clients (repeatable).uvx pdfluent-mcp uninstall (or setup --uninstall) removes only the pdfluent entry, and only when it was written by pdfluent-mcp (or with --force). It does not remove the Python package.Close the clients before you run setup or uninstall. Files are replaced atomically, but no portable call lets setup detect a client that saves the same file in the last instant; a save in that gap can be lost, and where hard links are unavailable the backup is a plain copy and the gap is wider.
Setup keeps unrelated settings and servers, preserves TOML comments, and refuses malformed configs and symbolic links. A different existing pdfluent entry is left alone unless you pass --force (SETUP-003). The saved launcher uses the absolute uvx path when found, otherwise the current Python interpreter, which must stay installed (SETUP-004).
Config files used: Claude Desktop's platform config (see above), Claude Code's ~/.claude.json, Cursor's ~/.cursor/mcp.json, and Codex's ~/.codex/config.toml (CODEX_HOME is respected).
Install into your own environment
pip install pdfluent-mcp
pdfluent-mcp --root ~/Documents
Allowed roots and limits
The server touches nothing outside its roots. Give them with --root (repeat for more than one), with PDFLUENT_MCP_ROOTS (:-separated, ; on Windows), or let the client offer them: Claude Code sends its working directory and --add-dir directories as MCP roots. A server with no root from any of these answers every call with a message saying so. Symlinks are resolved before the check, so a link inside a root cannot point outside it.
A root is as wide as the directory it names. A client that offers /, ~ or a drive as an MCP root widens the server to everything the user can read; if that is not what you want, start the server with --root on the folder your PDFs live in and keep the client's roots narrow.
Three more limits: --max-pages (default 200) is how many pages one call returns, so a long document is read in windows through pages (the engine still parses the whole document for every window; the cap bounds the output, not the work); --max-bytes (default 100 MiB) is the largest document the server opens; --max-output-chars (default 200000) is the longest result one call returns, after which it is cut and says so. All three also read from PDFLUENT_MCP_MAX_PAGES, PDFLUENT_MCP_MAX_BYTES and PDFLUENT_MCP_MAX_OUTPUT_CHARS.
Claude plugin and data handling
The repository is also a Claude plugin (.claude-plugin/plugin.json). It bundles the pdfluent skill from skills/ and starts the local MCP server with uvx pdfluent-mcp==0.3.0 over stdio, so the plugin runs one pinned PyPI package and nothing else. A local MCP server loads in Claude Code and in Cowork sessions on your computer; chat on claude.ai ignores it.
What the plugin does with data: the server reads PDFs only inside the directories you allow,
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