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sports-trading-card-agent

by rjUpdated May 4, 2026

Fetches real-time pricing for sports trading cards, performs market analysis, detects arbitrage opportunities, calculates grading ROI, provides investment advice, and retrieves player stats for NBA, NFL, and MLB. Offers 9 tools for AI agents to query card values and market data. Collectors, investors, and developers building trading applications use it to inform buying, selling, and portfolio decisions.

sports-cards
trading-cards
arbitrage
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Overview

The sports-trading-card-agent MCP server provides programmatic access to sports trading card market data, enabling AI agents to retrieve real-time prices, analyze trends, identify arbitrage, compute grading returns, generate investment recommendations, and pull player statistics across NBA, NFL, and MLB. It includes 9 specialized tools for precise data handling.

Key Capabilities

  • get_card_pricing: Retrieves current market prices for specific cards by player, set, and condition.
  • market_analysis: Analyzes historical price trends, volume, and volatility for cards or collections.
  • detect_arbitrage: Scans multiple marketplaces to find price discrepancies for profitable flips.
  • calculate_grading_roi: Estimates return on investment for sending cards to grading services like PSA or BGS.
  • investment_advice: Generates buy/sell/hold recommendations based on market data and player performance.
  • get_player_stats: Fetches career and recent stats for athletes to correlate with card value.
  • Additional tools cover population reports, comps lookup, and portfolio valuation.

Use Cases

  1. Arbitrage Trading: An AI agent uses detect_arbitrage and get_card_pricing to compare eBay and auction house listings, alerting users to cards undervalued by 20%+ for quick resale.

  2. Grading Decisions: Collectors query calculate_grading_roi with get_card_pricing data to determine if grading a raw NBA rookie card justifies the $50 fee based on post-grade value uplift.

  3. Portfolio Management: Investors run market_analysis and investment_advice on their MLB collection to rebalance holdings amid player trades or injuries.

  4. Player Performance Correlation: Using get_player_stats alongside market_analysis, agents predict card price surges for breakout NFL stars.

Who This Is For

Sports card collectors seeking data-backed acquisition strategies, investors tracking ROI on high-value assets, and developers integrating market intelligence into AI trading bots or advisory apps. Requires API keys for marketplaces like eBay or Card Ladder.