mcp-dharmamitra

mcp-dharmamitra

MCP server that runs OCR on an image via the public Dharmamitra OCR endpoint and writes the extracted text to a local file. Optimised for Tibetan / Sanskrit / Devanagari input.

Tool

ocr_image

Argument	Type	Default	Notes
image_path	str	—	Absolute path to a local image (png/jpg/…).
output_path	str	—	File to write UTF-8 text into. Parent dirs are created.
transliterate_devanagari_to_iast	bool	false	Passed to the API.
transliterate_tibetan_to_wylie	bool	false	Passed to the API.
instruction	str	""	Optional model instruction.
model	str	"auto"	OCR model id.
poll_interval_seconds	float	2.0	Delay between status polls.
timeout_seconds	float	300.0	Overall polling deadline.

Returns a small JSON summary — the extracted text is written to output_path, not returned to the client.

{
  "job_id": "b8b26984b3ca49199c28303cad6a144d",
  "output_path": "/abs/path/out.txt",
  "pages": 1,
  "processing_time_seconds": 3.957,
  "chars": 1234
}

Install

uv sync                           # or: pip install -e '.[dev]'

Run locally with the MCP inspector:

uv run mcp dev src/mcp_dharmamitra/server.py

Run tests:

uv run pytest

Wire into Claude Desktop / Claude Code

~/Library/Application Support/Claude/claude_desktop_config.json:

{
    "mcpServers": {
      "dharmamitra": {
        "command": "uv",
        "args": ["--directory", "/path/mcp-dharmamitra", "run", "mcp-dharmamitra"],
        "env": {"DHARMAMITRA_COOKIE": ""}
      }
    }
  }

Docker

Build the image:

docker build -t mcp-dharmamitra:latest .

MCP talks over stdio, so the container must be run with -i (no -t). Mount a host directory that holds the input images — the tool arguments (image_path, output_path) are paths inside the container.

Smoke-test the entrypoint:

docker run --rm --entrypoint python mcp-dharmamitra:latest \
  -c "from mcp_dharmamitra.server import mcp; print(mcp.name)"

Wire the container into Claude Desktop / Claude Code

Replace /Users/aleksei/projects/ai/mcp-dharmamitra/tmp with any host directory you want the tool to be able to read/write. Files inside it will be visible under /data in the container.

{
  "mcpServers": {
    "dharmamitra": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/Users/aleksei/projects/ai/mcp-dharmamitra/tmp:/data",
        "-e", "DHARMAMITRA_COOKIE",
        "mcp-dharmamitra:latest"
      ],
      "env": {
        "DHARMAMITRA_COOKIE": ""
      }
    }
  }
}

Or the same via CLI:

claude mcp add dharmamitra \
  --scope project \
  -e DHARMAMITRA_COOKIE="" \
  -- docker run -i --rm \
       -v /Users/aleksei/projects/ai/mcp-dharmamitra/tmp:/data \
       -e DHARMAMITRA_COOKIE \
       mcp-dharmamitra:latest

When calling the tool, pass container paths. Example — image at <host>/tmp/text.png → /data/text.png:

{
  "image_path": "/data/text.png",
  "output_path": "/data/text.txt"
}

The resulting text.txt appears in the mounted host directory.

Cloudflare / cf_clearance

The endpoint sits behind Cloudflare. Most requests go through without any cookie. If you start getting 403, grab a fresh cf_clearance value from a logged-in browser session on dharmamitra.org and export it:

export DHARMAMITRA_COOKIE='cf_clearance value here'

The server will attach it as a cookie on every request. Do not hardcode it — Cloudflare rotates the value.