🎯 Key Features
• Multi-Factor Analysis: Evaluates up to 17 distinct risk factors when data is available • Real-Time Assessment: Fast analysis with typical response times under 500ms • Robust Error Handling: Continues analysis even when some data sources are unavailable • Detailed Explanations: Human-readable explanations for each risk factor assessment • Threshold Transparency: Clear risk thresholds with configurable parameters available on request📊 Risk Assessment Framework
The API analyzes up to 17 risk factors across 5 core categories. Note that not all risk factors may be assessed for every token due to data availability from internal sources and calculations.🔑 Authority & Control (3 factors)
1. Circulating Supply Ratio- Purpose: Detects token dilution risk from centralized mint authority control
- Data Used:
circSupplyvstotalSupplyfields,audit.mintAuthorityDisabledflag - Calculation:
circulating_supply / total_supplyratio analysis - Risk Logic: Low circulating ratios indicate active mint authority usage, creating severe dilution potential
- Thresholds: HIGH < 80%, MEDIUM 80-95%, LOW >95%
- Why This Matters: Mint authorities can create unlimited new tokens, severely diluting existing holders without warning
- Purpose: Identifies trading restriction risk through account freezing capabilities
- Data Used:
freezeAuthorityfield,audit.freezeAuthorityDisabledflag - Calculation: Boolean presence check with audit override logic
- Risk Logic: Freeze authority allows token accounts to be frozen, preventing trading
- Thresholds: HIGH (present), LOW (absent or disabled)
- Why This Matters: Account freezing can trap user funds and prevent trading indefinitely
- Purpose: Assesses supply inflation risk from token creation capabilities
- Data Used:
mintAuthorityfield,audit.mintAuthorityDisabledflag - Calculation: Boolean presence check with audit override logic
- Risk Logic: Active mint authority enables unlimited token creation
- Thresholds: HIGH (present), LOW (absent or disabled)
- Why This Matters: New token minting can instantly devalue existing holdings through inflation
💰 Market Structure (5 factors)
4. Market Capitalization- Purpose: Evaluates manipulation susceptibility through market size analysis
- Data Used:
mcap(market capitalization) field - Calculation: Direct USD market cap comparison against thresholds
- Risk Logic: Smaller market caps are more susceptible to price manipulation
- Thresholds: HIGH < 1M-100M
- Why This Matters: Low market cap tokens require less capital to manipulate prices significantly
- Purpose: Identifies due diligence gaps increasing scam exposure
- Data Used:
isVerifiedboolean field - Calculation: Direct boolean evaluation
- Risk Logic: Unverified tokens lack institutional due diligence validation
- Thresholds: HIGH (unverified), LOW (verified)
- Why This Matters: Verification processes help filter out fraudulent or high-risk projects
- Purpose: Assesses exit execution difficulty and holder trapping risk
- Data Used:
liquidityUSD value field - Calculation: Direct USD liquidity comparison against thresholds
- Risk Logic: Low liquidity creates difficulty executing large trades
- Thresholds: HIGH < 10K-100K
- Why This Matters: Insufficient liquidity can trap holders unable to exit positions
- Purpose: Measures ownership distribution and manipulation resistance
- Data Used:
holderCountfield - Calculation: Direct holder count comparison against thresholds
- Risk Logic: Few holders indicate high concentration and easier market control
- Thresholds: HIGH < 100, MEDIUM 100-1000, LOW >1000
- Why This Matters: Concentrated ownership enables coordinated price manipulation
- Purpose: Detects extreme concentration enabling coordinated dump attacks
- Data Used:
audit.topHoldersPercentagefield - Calculation: Percentage of total supply held by top holders
- Risk Logic: High concentration by top holders enables catastrophic coordinated selling
- Thresholds: HIGH >90%, MEDIUM 80-90%, LOW < 80%
- Why This Matters: Large holders can cause devastating price crashes through coordinated selling
📈 Trading Patterns (3 factors)
9. Price Volatility- Purpose: Identifies market instability or manipulation through price swing analysis
- Data Used:
priceChangefromstats5m,stats1h,stats6h,stats24hfields - Calculation: Maximum absolute price change across all timeframes
- Risk Logic: Extreme price swings suggest instability or active manipulation
- Thresholds: HIGH >50%, MEDIUM 20-50%, LOW < 20%
- Why This Matters: High volatility indicates unstable markets or potential manipulation
- Purpose: Detects artificial trading activity creating false liquidity impressions
- Data Used:
buyVolumeandsellVolumefromstats1h,stats6h,stats24hfields - Calculation: Buy/sell volume balance analysis across timeframes
- Risk Logic: Suspiciously balanced buy/sell volumes indicate artificial trading
- Thresholds: HIGH (suspicious in ≥2 timeframes), LOW (normal patterns)
- Why This Matters: Wash trading creates false impressions of trading activity and demand
- Purpose: Evaluates authentic demand vs bot-driven trading activity
- Data Used:
organicScoreLabelfield - Calculation: Direct label evaluation (high/medium/low)
- Risk Logic: Low organic scores suggest bot-driven rather than genuine trading
- Thresholds: HIGH (low organic), MEDIUM (medium organic), LOW (high organic)
- Why This Matters: Bot-driven trading indicates lack of genuine user interest and adoption
🚀 Platform & Social (4 factors)
12. Developer Migrations- Purpose: Assesses project stability through team migration history
- Data Used:
audit.devMigrationsfield - Calculation: Count of developer team migrations
- Risk Logic: Frequent migrations indicate team instability and abandonment risk
- Thresholds: HIGH ≥5, MEDIUM 2-4, LOW < 2
- Why This Matters: Team instability often precedes project abandonment
- Purpose: Validates legitimacy through institutional due diligence requirements
- Data Used:
cexes(centralized exchanges) array field - Calculation: Count and quality of exchange listings
- Risk Logic: Major exchanges require due diligence, providing legitimacy validation
- Thresholds: HIGH (no listings), MEDIUM (< 3 or no major), LOW (major exchanges)
- Why This Matters: Exchange listings indicate institutional validation and due diligence
- Purpose: Identifies platform reputation risk through historical correlation analysis
- Data Used:
launchpad,platformfields, mint address patterns - Calculation: Platform identification and risk correlation lookup
- Risk Logic: Some platforms have higher historical rates of fraudulent launches
- Thresholds: HIGH (high-risk platforms like pump.fun), LOW (other platforms)
- Why This Matters: Platform choice correlates with project quality and fraud rates
- Purpose: Evaluates team transparency and accountability mechanisms
- Data Used:
twitter,telegram,websitefields - Calculation: Boolean presence check across social platforms
- Risk Logic: Social presence provides transparency and accountability channels
- Thresholds: HIGH (no presence), LOW (active presence)
- Why This Matters: Team transparency enables accountability and reduces exit scam risk
⏰ Temporal (2 factors)
16. Token Age- Purpose: Assesses maturity and operational track record
- Data Used:
firstPool.createdAttimestamp field - Calculation: Days since first liquidity pool creation
- Risk Logic: New tokens lack operational history and higher experimental risk
- Thresholds: HIGH < 7 days, MEDIUM 7-30 days, LOW > 30 days
- Why This Matters: Time provides validation of project sustainability and reduces rug pull risk
- Purpose: Evaluates transition volatility in recently promoted tokens
- Data Used:
graduatedAttimestamp field - Calculation: Days since launchpad graduation
- Risk Logic: Recent graduations often create volatility during market transitions
- Thresholds: MEDIUM < 7 days, LOW >7 days or N/A
- Why This Matters: Graduation transitions can create price volatility and instability
🔢 Risk Scoring System
Scoring Methodology
- Factor Scoring: LOW = 0 points, MEDIUM = 1 point, HIGH = 2 points
- Total Score: Sum of all assessed factor scores (maximum 34 points for 17 factors)
- Risk Percentage:
(total_score / max_possible_score) × 100
Overall Risk Classification
- 🔴 HIGH RISK: ≥60% of maximum possible score
- 🟡 MEDIUM RISK: ≥30% but < 60% of maximum possible score
- 🟢 LOW RISK: < 30% of maximum possible score
Custom Thresholds
Current thresholds are calibrated for optimal risk detection. Custom threshold injection is available for enterprise clients—contact info@range.org to request this feature.📡 Data Sources & Availability
Token data is sourced from internal data aggregation and calculations combining multiple blockchain data providers. The service analyzes available data fields dynamically: Available Data Fields:circSupply, totalSupply, freezeAuthority, mintAuthority, mcap, isVerified, liquidity, holderCount, audit.topHoldersPercentage, stats*, organicScoreLabel, cexes, launchpad, twitter, telegram, website, firstPool.createdAt, graduatedAt
Data Availability: Not all data fields are available for every token. The API gracefully handles missing data by:
- Skipping unavailable risk factors
- Including detailed error messages explaining missing assessments
- Continuing analysis with available data
- Providing partial results with warnings when appropriate
⚡ Response Format
Low-Risk Token Example (USDC)
Medium-Risk Token Example (Limited Data)
🚨 API Endpoint
Request
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
mint_address | string | Yes | Solana token mint address (32-44 character base58 string) |
network | string | No | Blockchain network (default: “solana”) - currently only ‘solana’ is supported |
Headers
Response Structure
Success Response (200)
Error Response (400/500)
🔧 Error Handling Examples
Invalid Address Format
Network Error
Response Features
- Graceful Degradation: Returns partial results when some risk factors cannot be assessed
- Detailed Error Messages: Specific explanations for each failed assessment in the
errorsarray - Transparent Processing: Non-fatal errors included in response for troubleshooting
- Consistent Structure: All successful responses follow the same JSON schema
🔄 Service Evolution
This is a constantly evolving service with continuous improvements:- New Risk Factors: Additional risk factors are regularly researched and implemented
- Enhanced Detection: Machine learning integration and advanced pattern recognition
- Threshold Optimization: Regular calibration based on market conditions and feedback
- Data Source Expansion: Integration with additional data providers for better coverage
💡 Use Cases
Compliance & Risk Management
- Screen tokens before integration into DeFi protocols
- Assess compliance risk for regulatory requirements
- Evaluate tokens for institutional investment policies
DeFi & Trading Applications
- Pre-trade risk assessment for automated strategies
- Portfolio risk monitoring and rebalancing
- User protection in DeFi applications
Security & Due Diligence
- Token vetting for exchange listings
- Investment research and due diligence
- Fraud prevention and scam detection
📝 Example Requests & Responses
Stablecoin Assessment (Low Risk)
Request:Utility Token Assessment
Request:Additional Test Tokens
Popular Stablecoins:
- USDT:
Es9vMFrzaCERmJfrF4H2FYD4KCoNkY11McCe8BenwNYB - Wrapped SOL:
So11111111111111111111111111111111111111112
Established Meme Tokens:
- BONK:
DezXAZ8z7PnrnRJjz3wXBoRgixCa6xjnB7YaB1pPB263 - WIF:
EKpQGSJtjMFqKZ9KQanSqYXRcF8fBopzLHYxdM65zcjm - POPCAT:
7GCihgDB8fe6KNjn2MYtkzZcRjQy3t9GHdC8uHYmW2hr
DeFi & Infrastructure:
- Pyth Network:
HZ1JovNiVvGrGNiiYvEozEVgZ58xaU3RKwX8eACQBCt3 - Marinade SOL:
mSoLzYCxHdYgdzU16g5QSh3i5K3z3KZK7ytfqcJm7So - JitoSOL:
J1toso1uCk3RLmjorhTtrVwY9HJ7X8V9yYac6Y7kGCPn
Meme Token Assessment
Request:Limited Data Token Assessment
Request:🌐 Network Support
Currently Supported:- ✅ Solana - Full risk assessment with 17+ risk factors
- 🔄 Ethereum - ERC-20 token risk assessment (in development)
- 🔄 Base - Layer 2 token analysis (roadmap)
- 🔄 Arbitrum - Rollup token evaluation (roadmap)
- 🔄 Polygon - Multi-chain compatibility (roadmap)
📞 Support & Contact
For technical support, enterprise features, or custom threshold configurations:- Email: info@range.org
- Documentation: This reference guide
- Response Time: Typically under 500ms for standard requests
📊 Risk Factor Summary Table
| Factor | Category | Data Source | HIGH Risk | MEDIUM Risk | LOW Risk |
|---|---|---|---|---|---|
| Circulating Supply Ratio | Authority & Control | circSupply/totalSupply | < 80% | 80-95% | >95% |
| Freeze Authority | Authority & Control | freezeAuthority | Present | - | Absent/Disabled |
| Mint Authority | Authority & Control | mintAuthority | Present | - | Absent/Disabled |
| Market Capitalization | Market Structure | mcap | < $1M | 100M | >$100M |
| Token Verification | Market Structure | isVerified | Unverified | - | Verified |
| Liquidity | Market Structure | liquidity | < $10K | 100K | >$100K |
| Holder Count | Market Structure | holderCount | < 100 | 100-1000 | >1000 |
| Top Holder Concentration | Market Structure | audit.topHoldersPercentage | >90% | 80-90% | < 80% |
| Price Volatility | Trading Patterns | stats*.priceChange | >50% | 20-50% | < 20% |
| Wash Trading | Trading Patterns | stats*.buyVolume/sellVolume | Suspicious ≥2 TF | - | Normal |
| Organic Activity | Trading Patterns | organicScoreLabel | Low | Medium | High |
| Developer Migrations | Platform & Social | audit.devMigrations | ≥5 | 2-4 | < 2 |
| Exchange Listings | Platform & Social | cexes | No listings | < 3/no major | Major exchanges |
| Launchpad Platform | Platform & Social | launchpad/patterns | High-risk (pump.fun) | - | Other platforms |
| Social Media Presence | Platform & Social | twitter/telegram/website | No presence | - | Active presence |
| Token Age | Temporal | firstPool.createdAt | < 7 days | 7-30 days | >30 days |
| Graduation Status | Temporal | graduatedAt | - | < 7 days | >7 days/N/A |
This documentation covers the Token Risk Assessment API as of September 2025. Features and thresholds are subject to continuous improvement and calibration.
Authorizations
Authorization method required to allow user to access the api endpoints.
Query Parameters
Asset identifier (chain-agnostic). Currently only Solana token mint (base58, 32–44 chars) is supported.
Example:
"So11111111111111111111111111111111111111112"
Blockchain network for token risk (only solana supported)
Available options:
solana Example:
"solana"
Response
200 - application/json
Token risk assessment with detailed factors and scoring
Basic token identification details
Overall risk assessment aggregation
Summary counts of risk factor severity levels
Map of risk factor key to its detailed assessment
Processing time in milliseconds for the assessment
Example:
154
List of non-fatal errors encountered while computing the assessment

