doc-extract-mcp

An MCP (Model Context Protocol) server that gives an LLM deterministic document tooling for structured-data extraction workflows. The LLM does the reading and extraction reasoning; this server provides the parts that should never be left to a language model: reliable file access, parsing, chunking, JSON Schema validation, and guarded file output.

Built by Koray Nar as a portfolio project for the AI document-automation workflows he is building — the target use case is turning messy PDFs (purchase orders, invoices, reports) into schema-validated JSON. Published as part of a public portfolio. Pairs with Claude Code and Claude Desktop, and with any other MCP client.

Why

An extraction agent fails in predictable places: it hallucinates file contents, loses track of long documents, silently produces JSON that almost matches the target schema, and writes output wherever it likes. This server removes those failure modes:

File access is confined to one allowed root (DOC_EXTRACT_ROOT).
PDF text arrives with explicit --- page N --- markers, so citations of "page 3" mean page 3.
Long documents are chunked deterministically with overlap and page hints.
Extracted JSON is checked against a JSON Schema (Draft 2020-12) and every error is reported with a JSON Pointer path — not just the first — so the model can fix all mistakes in one pass.
Output is written by the server (JSON or CSV), inside the same root, with a verifiable row/byte count.

Tools

Tool	Arguments	What it does
`list_documents`	`directory`, `glob_pattern='*'`	List files under a directory inside the allowed root, with size and modified time. Supports recursive globs like `*/.pdf`. Patterns must be relative and free of `..`; matches resolving outside the root are dropped.
`read_document`	`path`, `pages=''`	Return a document's text. `.pdf` via pypdf with `--- page N ---` markers and optional 1-indexed page selection (`'3'`, `'1-5'`, `'1-3,7'`); `.txt`/`.md`/`.json` read directly; `.csv` rendered as an aligned text table. Clear error for unsupported types.
`document_info`	`path`	Metadata without full content: type, size, modified time; page count and PDF metadata for PDFs; line count for text files.
`chunk_document`	`path`, `max_chars=4000`, `overlap=200`	Split a document into ordered overlapping chunks, each with an index, start offset, and (for PDFs) a page hint.
`validate_json`	`data`, `json_schema`	Validate a JSON string against a JSON Schema (Draft 2020-12). Returns every validation error with a JSON Pointer path via `Draft202012Validator.iter_errors`.
`save_structured`	`path`, `data`, `format='json'\|'csv'`	Write extracted data inside the allowed root. CSV expects a JSON array of flat objects. Returns written path, row count, and byte count.

All path arguments are resolved and refused if they escape the allowed root (path traversal guard). The glob_pattern argument is confined the same way: absolute patterns and patterns containing .. are rejected, and any match that resolves outside the root (for example through a symlink) is silently dropped from the listing. Guard failures are raised as MCP tool errors, so the calling model sees the actual reason, not a masked generic error.

Quickstart

Requires Python 3.11+ and uv.

git clone https://github.com/koraynar/doc-extract-mcp.git
cd doc-extract-mcp
uv venv
uv pip install -e .

Run standalone (stdio transport):

DOC_EXTRACT_ROOT=/path/to/your/documents uv run doc-extract-mcp

Claude Code

claude mcp add doc-extract --env DOC_EXTRACT_ROOT=/path/to/your/documents \
  -- uv run --directory /absolute/path/to/doc-extract-mcp doc-extract-mcp

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "doc-extract": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/doc-extract-mcp",
        "doc-extract-mcp"
      ],
      "env": {
        "DOC_EXTRACT_ROOT": "/path/to/your/documents"
      }
    }
  }
}

DOC_EXTRACT_ROOT defaults to the server's working directory if unset. Set it to the folder your documents live in; nothing outside it can be read or written.

Typical workflow

list_documents(".", "*.pdf") — find the invoices.
document_info("invoice.pdf") — check the page count.
read_document("invoice.pdf", "1-3") or chunk_document(...) — get text.
The LLM extracts fields into JSON.
validate_json(data, json_schema) — fix every reported error, revalidate.
save_structured("out/invoice.json", data, "json") — write the result.

Limitations (honest ones)

Text-based PDFs only. Extraction uses pypdf; scanned/image-only PDFs yield empty text. There is no OCR.
Extraction quality varies with how the PDF was produced. Complex layouts (multi-column, heavy tables) may come out with imperfect reading order — that is a pypdf characteristic this server inherits.
No .docx / .xlsx support. Supported types are .pdf, .txt, .md, .csv, .json.
The server does no extraction reasoning. It will not find your invoice total; it makes sure the model that does is working from real text and that the result matches your schema.
This is a working tool, built for the AI-automation work I'm building up and published as part of my portfolio — it is new and has no production mileage yet. It has tests and a path-confinement guard, but it has not been hardened beyond that — review before pointing it at sensitive directories.

Development

uv venv
uv pip install -e '.[dev]'
uv run pytest

The test suite builds a small two-page PDF fixture in-memory (a minimal hand-constructed PDF, no extra dependencies) and covers all six tools, the path-traversal guard, glob-pattern confinement (including symlink escapes), page-range errors, multi-error schema validation, a CSV round-trip, and tool registration plus error propagation through the MCP server object.

doc extract mcp