Build an Autonomous Pokemon Card Trading Agent with AI Grading & Monte Carlo Pricing

Sailor Pepe — Sun, 12 Apr 2026 02:10:57 +0000

Give your AI agent real-time market intelligence across 187K+ trading card products. Search, price, and grade cards -- all from Python.

If you're building an AI agent that deals with trading cards -- whether it's a personal shopping bot, a portfolio tracker, or an autonomous collector -- you need real-time market data. Not mockups. Not CSV dumps. Live prices.

I built TCG Oracle, an API that tracks 187,689 products across 13 card games (Pokemon, Magic, Yu-Gi-Oh, Lorcana, One Piece, and more). Today I'm open-sourcing the tool wrappers so you can drop TCG intelligence into any AI agent in under 60 seconds.

Install

pip install tcg-oracle-tools

That's it. No API keys. No signup. Free tier gives you search and market data.

Quick Start

from tcg_oracle import TCGOracleClient

client = TCGOracleClient()

results = client.search("Charizard", limit=5)
for card in results.results:
    print(f"{card.name}: ${card.market_price}")

Output:

Mega Charizard Y ex - 294/217: $439.69
Mega Charizard Y ex - 022/217: $6.97

Market Snapshot

market = client.market("Pokemon")
print(f"Tracking {market.total_products} products")
for card in market.top_cards[:5]:
    print(f"  {card.name}: ${card.market_price}")

Drop Into a LangChain Agent

from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI
from tcg_oracle.langchain import TCGSearchTool, TCGMarketTool

tools = [TCGSearchTool(), TCGMarketTool()]
agent = initialize_agent(tools, ChatOpenAI(model="gpt-4o-mini"), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
agent.run("What's the most expensive Pokemon card right now?")

OpenAI Function Calling

from tcg_oracle.openai_spec import tools, handle_tool_call
response = openai.chat.completions.create(model="gpt-4o", messages=[{"role": "user", "content": "Find Pikachu cards"}], tools=tools())

The Agent-to-Agent Economy

TCG Oracle is registered on Virtuals Protocol ACP v2. Autonomous agents can discover and hire it directly, paying USDC micropayments on Base.

AI Card Grading: $0.05 per card
Monte Carlo Simulation: $0.10 per run
Search and Market Data: Free

PyPI: https://pypi.org/project/tcg-oracle-tools/
Agent: https://app.virtuals.io/acp/agents/019d7e15-6548-7504-ab29-57ebea3960ca
API: https://methods-supplier-foundation-stuck.trycloudflare.com/openapi.json

Built by The Undesirables LLC.

I Built a Zero-Trust MCP Server With 30+ Local AI Tools

Sailor Pepe — Sun, 05 Apr 2026 14:08:16 +0000

Why I Built This

I got tired of AI tools that send your data to the cloud. I wanted something that runs entirely on my machine — no API keys to OpenAI, no data leaving my computer, no monthly subscriptions.

So I built an MCP server with 30+ tools that all run locally. It started as a tool to grade my trading card collection using vision AI, and grew into a full production suite.

Install it now:

pip install undesirables-mcp-server

What It Does

🎴 AI Card Grading

Point a camera at your Pokémon, Magic, or Yu-Gi-Oh card. The server uses Qwen-VL vision AI to analyze centering, surface quality, corners, and edges — then predicts a PSA/Beckett grade. All inference runs on Ollama, locally.

📊 Monte Carlo Price Simulations

Real stochastic models (Heston, Merton, Kou, GBM) running against actual TCGCSV market data. See 150 possible price futures for any card in the market, grounded in empirical drift and volatility.

🖼️ Local Image Generation

Uses FLUX.1-schnell for image generation:

Mac (Apple Silicon): MLX backend via mflux — no Python PyTorch needed
Windows: DirectML for AMD/Intel GPUs
Linux: CUDA for Nvidia GPUs

🎵 AI Music Generation

Full song generation via ACE-Step. Specify genre, lyrics, duration — get a complete audio file rendered locally.

🎬 Video Production

Beat-synchronized video editing with librosa audio analysis. Extract viral clips, add text overlays, sync effects to music drops.

🔒 Security Scanning

Built-in SAST auditing for smart contracts and codebases. Path-validated, sandboxed execution.

🧠 RAG Memory Engine

Vector-indexed memory with semantic search. Your AI agent remembers conversations, builds knowledge graphs, and maintains persistent context.

Architecture

┌─────────────────────────────────────────────┐
│           MCP Client (Cursor, Claude)       │
└──────────────────┬──────────────────────────┘
                   │ JSON-RPC (stdio)
┌──────────────────▼──────────────────────────┐
│        Undesirables MCP Server              │
│  ┌──────────┐ ┌──────────┐ ┌────────────┐  │
│  │Resources │ │  Tools   │ │  Prompts   │  │
│  │Soul Data │ │30+ Tools │ │Templates   │  │
│  │Memory    │ │Vision AI │ │            │  │
│  │RAG Index │ │Audio/Vid │ │            │  │
│  └──────────┘ └────┬─────┘ └────────────┘  │
└────────────────────┼────────────────────────┘
                     │ HTTP (localhost)
┌────────────────────▼────────────────────────┐
│              Ollama (Local LLM)             │
│         qwen2.5 / llama3.1 / gemma3        │
└─────────────────────────────────────────────┘

Zero-Trust by Design

No cloud calls. Every AI model runs on your hardware via Ollama
No telemetry. Zero analytics, zero tracking
Sandboxed execution. Path validation prevents directory traversal attacks
Memory-locked. Resource limits prevent DoS from malicious prompts
BSL-1.1 licensed. Open source with commercial protections

Getting Started

# Install
pip install undesirables-mcp-server

# Or clone from source
git clone https://gitlab.com/meme-merchants/undesirables-mcp-server.git
cd undesirables-mcp-server
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Connect to Claude Desktop

Add this to your Claude config (Settings > Developer > Edit Config):

{
  "mcpServers": {
    "undesirables": {
      "command": "python",
      "args": ["server.py"],
      "cwd": "/path/to/undesirables-mcp-server"
    }
  }
}

Forem: Sailor Pepe