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

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 from the MCP registry

Listed as io.github.alex-deus/mcp-dharmamitra on the official Model Context Protocol registry. The package ships to PyPI as mcp-dharmamitra and is meant to be run with uvx — no clone, no manual install.

~/Library/Application Support/Claude/claude_desktop_config.json (or the equivalent mcp block in Claude Code):

{

"mcpServers": { "dharmamitra": { "command": "uvx", "args": ["mcp-dharmamitra"], "env": {"DHARMAMITRA_COOKIE": ""} } } }

Or via the Claude Code CLI:

claude mcp add dharmamitra -e DHARMAMITRA_COOKIE="" -- uvx mcp-dharmamitra

Pin a specific version with uvx mcp-dharmamitra@0.1.0.

Install from source

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

For a source checkout the Claude Desktop entry uses the local path:

{

"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 /path/mcp-dharmamitra 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", "/path/mcp-dharmamitra/data:/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 /path/mcp-dharmamitra/data:/data \ -e DHARMAMITRA_COOKIE \ mcp-dharmamitra:latest

When calling the tool, pass container paths. Example — image at /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.