Token Risk Assessment

via “structural risk signal detection”

Evaluate crypto token safety with real-time trust scores and structural risk signals. Identify potential market distress and impending collapses to safeguard your digital investments. Compare assets head-to-head using multi-dimensional security and compliance metrics.

Unique: Uses multi-layer pattern matching combining bytecode-level analysis (via EVM opcode inspection), semantic contract analysis (via AST parsing of verified source), and ecosystem topology analysis (via on-chain relationship graphs) to detect risks that single-layer approaches miss, such as cross-contract reentrancy or cascading liquidity risks

vs others: Provides explainable, categorized risk signals with severity levels and remediation guidance (not just a pass/fail audit), enabling developers to build nuanced risk policies that distinguish between critical code vulnerabilities and manageable economic risks

via “vault-risk-assessment-and-scoring”

AI-native access to aarna's tokenized yield vaults on Ethereum and Base. 20 tools for vault discovery, performance metrics, transaction building, and portfolio tracking.

Unique: Computes multi-dimensional risk scores (smart contract, liquidity, concentration, governance) from on-chain data and produces a composite risk score. Enables risk-aware vault filtering without requiring manual risk analysis.

vs others: More comprehensive than simple TVL-based risk assessment because it evaluates multiple risk dimensions; more accessible than building custom risk models because it returns pre-computed risk scores.

via “real-time token risk scoring”

Real-time Solana token risk scoring and pump.fun graduation signals for AI assistants and trading agents. Built by Sol, an autonomous AI agent. 6 tools: get_token_risk (0-100 risk score + rug pull flags), get_momentum_signal (BUY/SELL based on buy/sell ratios), batch_token_risk (screen up to 10 tok

Unique: Employs a machine learning model trained on historical transaction data specific to Solana, enhancing predictive capabilities for risk assessment.

vs others: More accurate than generic risk scoring tools due to its focus on Solana-specific metrics and behaviors.

via “risk score evaluation and quantification”

Evaluate risk scores and simulate outcomes to make informed business decisions. Automate policy enforcement using specialized decision endpoints for secure transaction management. Streamline governance by integrating real-time gating into your automated workflows.

Unique: Exposes risk evaluation as standardized MCP tool endpoints, enabling any MCP-compatible client (Claude, custom agents, workflow engines) to invoke risk models without SDK dependencies or direct model access. Decouples risk model deployment from client application logic.

vs others: Unlike point-solution fraud APIs (Stripe Radar, Kount), ActionGate's MCP abstraction allows teams to plug in proprietary or open-source risk models and integrate scoring into broader agent-driven workflows without vendor lock-in.

via “three-tier risk assessment generation”

Verify Australian and New Zealand businesses against government registers via any MCP-compatible AI agent. Returns registration status, directors, licences, trading names, and a three-tier risk assessment (CLEAR / ADVISORY / FLAGS_FOUND) that surfaces regulatory findings across jurisdictions — incl

Unique: Employs a unique heuristic-based methodology to categorize compliance risks, providing a structured output that enhances decision-making.

vs others: Offers a more nuanced risk assessment framework compared to basic verification tools, allowing for better-informed compliance decisions.

# Rug Munch Intelligence — MCP Server [](https://modelcontextprotocol.io) [](https://cryptorugmunch.app/api/agent/v1/status) [](https://

Unique: Integrates social media sentiment analysis with on-chain data to provide a comprehensive risk score, unlike traditional methods that rely solely on historical price data.

vs others: More comprehensive than basic token analysis tools as it combines multiple data sources for risk evaluation.

via “single token risk assessment”

Tools: - scan_token - Scan a single token for rug pull risk, honeypot status, and temporal analysis - batch_scan - Scan up to 10 tokens in parallel - health_check - Check API and model availability - compare_rugcheck - Compare DrainBrain ML score vs RugCheck heuristic side-by-side Install:

Unique: Utilizes a specialized machine learning model designed for real-time risk evaluation of cryptocurrency tokens, which is continuously updated with new data.

vs others: More accurate than traditional heuristic methods due to its machine learning foundation that adapts to new patterns.

via “wallet risk scoring”

AI-powered XRPL wallet risk scoring. Score any wallet before you move money — pay per call in XRP via x402. No API keys needed

Unique: Utilizes a proprietary machine learning model specifically trained on XRPL transaction data, allowing for real-time risk scoring without the need for user authentication or API keys.

vs others: More accessible than traditional risk assessment APIs since it eliminates the need for API keys and offers pay-per-call pricing in XRP.

via “portfolio risk assessment”

MCP server: stock-predictions

Unique: Utilizes Monte Carlo simulations tailored to individual portfolios, providing a more personalized risk assessment than standard models.

vs others: Delivers deeper insights into portfolio risk compared to traditional risk calculators by simulating various market scenarios.

via “risk-scoring-and-assessment”

via “real-time-risk-scoring”

via “risk-assessment-and-scoring”

via “risk-factor-identification”

via “risk-profiling-and-assessment”

via “property risk modeling”

via “risk assessment and scoring”

via “contextual-risk-assessment”

via “risk-assessment-for-properties”

via “automated portfolio risk assessment”

via “risk-assessment-for-changes”