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.

Related MCP server: BigContext MCP

Tools

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

1. list_documents(".", "*.pdf") — find the invoices.

2. document_info("invoice.pdf") — check the page count.

3. read_document("invoice.pdf", "1-3") or chunk_document(...) — get text.

4. The LLM extracts fields into JSON.

5. validate_json(data, json_schema) — fix every reported error, revalidate.

6. 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.

License

MIT © 2026 Koray Nar