Card Grader MCP

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Vision-based trading card identification, centering analysis, and grading assessment.

Supported card types: Pokémon TCG, Magic: The Gathering, Yu-Gi-Oh!, sports cards (Topps, Panini, Upper Deck, Bowman, etc.)

Grading services: CGC, PSA, BGS, SGC, TAG

Tools

Setup

Environment Variables

$exportANTHROPIC_API_KEY="sk-ant-..."

Set in Apify Console → Source → Environment Variables (mark as secret).

Local Development

cd card-grader
python3 -m venv venv
source venv/bin/activate
pip install-r requirements.txt
exportANTHROPIC_API_KEY="sk-ant-..."
python run_server.py

Server starts on http://localhost:4321/mcp

Testing

Encode a test image

$BASE64=$(base64 -w0 /path/to/card.jpg)

Test identify_card

from server import identify_card
import base64
withopen("card.jpg","rb")as f:
 b64 = base64.b64encode(f.read()).decode()
result = identify_card(image_data=b64, mime_type="image/jpeg")
print(result)

Test analyze_centering

from server import analyze_centering
import base64
withopen("card.jpg","rb")as f:
 b64 = base64.b64encode(f.read()).decode()
result = analyze_centering(image_data=b64)
print(result["centering"])

Test estimate_card_value (no image)

from server import estimate_card_value
result = estimate_card_value(
 card_name="Charizard",
 card_set="Base Set",
 card_number="4/102",
 year=1999,
 rarity="holo rare",
 card_variant="shadowless",
 condition="near mint raw",
)
print(result)

Test grade_assessment

from server import grade_assessment
import base64
withopen("card.jpg","rb")as f:
 b64 = base64.b64encode(f.read()).decode()
result = grade_assessment(
 image_data=b64,
 target_grade="10",
 grading_service="CGC",
)
print(result["assessment"]["submission_recommendation"])
print(result["assessment"]["cgc_summary"])

Test identify_card_both_sides

from server import identify_card_both_sides
import base64
withopen("front.jpg","rb")as f:
 front_b64 = base64.b64encode(f.read()).decode()
withopen("back.jpg","rb")as f:
 back_b64 = base64.b64encode(f.read()).decode()
result = identify_card_both_sides(
 front_image_data=front_b64,
 back_image_data=back_b64,
)
print(result["card"]["cgc_summary"])

Deploy to Apify

rm-rf venv
apify push

After push: re-enable Actor Standby in Apify Console → Settings.

Claude Desktop config

{
"mcpServers":{
"card-grader":{
"command":"npx",
"args":["-y","mcp-remote","https://YOUR_USERNAME--card-grader.apify.actor/mcp?token=apify_api_XXXXX"]
}
}
}

CGC Centering Scale Reference

Note: Centering is measured from photos. Corners, edges, and surface require hands-on inspection for accurate sub-grades.

License

MIT — Mowell Labs 2025