# Card Grader MCP (`mowell_labs/card-grader`) Actor Vision-based trading card identification, centering analysis, and grading assessment. Supports Pokémon, Magic: The Gathering, Yu-Gi-Oh!, and sports cards. - **URL**: https://apify.com/mowell\_labs/card-grader.md - **Developed by:** [Brian Mowell](https://apify.com/mowell_labs) (community) - **Categories:** E-commerce, MCP servers, Agents - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $30.00 / 1,000 card identification (single photo)s This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Card Grader MCP 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 | Tool | Description | PPE | |------|-------------|-----| | `identify_card` | Single front photo → name, set, rarity, centering, value | $0.03 | | `identify_card_both_sides` | Front + back → full centering both sides + damage | $0.05 | | `analyze_centering` | Centering-only analysis → L/R, T/B ratios, CGC/PSA/BGS sub-grades | $0.03 | | `estimate_card_value` | Text-only value lookup (no image needed) | $0.02 | | `grade_assessment` | Full submission assessment → submit/borderline/skip + reasoning | $0.04 | --- ### Setup #### Environment Variables ```bash export ANTHROPIC_API_KEY="sk-ant-..." ```` Set in Apify Console → Source → Environment Variables (mark as secret). #### Local Development ```bash cd card-grader python3 -m venv venv source venv/bin/activate pip install -r requirements.txt export ANTHROPIC_API_KEY="sk-ant-..." python run_server.py ``` Server starts on `http://localhost:4321/mcp` *** ### Testing #### Encode a test image ```bash BASE64=$(base64 -w 0 /path/to/card.jpg) ``` #### Test identify\_card ```python from server import identify_card import base64 with open("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 ```python from server import analyze_centering import base64 with open("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) ```python 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 ```python from server import grade_assessment import base64 with open("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 ```python from server import identify_card_both_sides import base64 with open("front.jpg", "rb") as f: front_b64 = base64.b64encode(f.read()).decode() with open("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 ```bash rm -rf venv apify push ``` After push: re-enable Actor Standby in Apify Console → Settings. #### Claude Desktop config ```json { "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 | Sub-Grade | L/R & T/B | |-----------|-----------| | 10 (Pristine) | 55/45 or better | | 9.5 (Gem Mint) | 60/40 or better | | 9 (Mint) | 65/35 or better | | 8.5 (NM/Mint+) | 65/35–70/30 | | 8 (NM/Mint) | 70/30–75/25 | | 7.5 and below | Worse than 75/25 | **Note:** Centering is measured from photos. Corners, edges, and surface require hands-on inspection for accurate sub-grades. *** ### License MIT — Mowell Labs 2025 ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("mowell_labs/card-grader").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("mowell_labs/card-grader").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call mowell_labs/card-grader --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mowell_labs/card-grader", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Card Grader MCP", "description": "Vision-based trading card identification, centering analysis, and grading assessment. Supports Pokémon, Magic: The Gathering, Yu-Gi-Oh!, and sports cards.", "version": "1.0", "x-build-id": "j7C1u2B6lIcKq7tKR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mowell_labs~card-grader/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mowell_labs-card-grader", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mowell_labs~card-grader/runs": { "post": { "operationId": "runs-sync-mowell_labs-card-grader", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mowell_labs~card-grader/run-sync": { "post": { "operationId": "run-sync-mowell_labs-card-grader", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": {} }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```