Discover and filter a daily list of attractive tokens using Nansen Score Indicators weighted by coefficients (= Performance Score).
Use this tool when you don't know which tokens to buy and need recommendations based on backtested indicators.
For specific token analysis (e.g., "should I buy AAVE?"), use token_quant_scores instead.
**When to use this tool vs token_discovery_screener**:
- Use **this tool** when you want **pre-scored buying recommendations** without specifying criteria. It answers "what should I buy?" by returning tokens that already meet a quantitative buying threshold (Performance Score ≥15) based on alpha indicators like price momentum, chain fees, and protocol fees. Data is updated in batches.
- Use **token_discovery_screener** when you want **live data** or to **explore tokens by specific criteria** like sectors (e.g., "AI memecoins"), token age (e.g., "new launches"), smart money activity, or custom volume/liquidity thresholds. It's a filtering tool with real-time metrics where you define what you're looking for.
Returns tokens pre-filtered by: performance_score >= 15 (buying threshold).
**Example queries**: "what tokens should I buy?", "which tokens look good?", "best tokens to buy today"
**Scoring:**
- **Performance Score** (range -60 to +75): Higher = better alpha opportunity. **Buy threshold: ≥15**
- **Risk Score** (range -60 to +80): Higher = safer token. >0 indicates low to medium risk.
Every time you give the Performance Score to the user, explain the scoring thresholds above. Same for the Risk Score. Every time quote the underlying indicators that contributed the most to the Performance/ Risk score and recall their definition to the user.
Returns:
A list of tokens with the highest Performance Score as markdown.
Core fields: Token Address, Token Symbol, Chain, Performance Score, Risk Score.
Indicator columns are included dynamically based on data availability (columns with all zeros are excluded).