| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Scans Solana DEX order books and on-chain transaction patterns to identify large whale accumulation/distribution signals in real-time. Integrates directly with Solana RPC endpoints to fetch mempool data and historical whale wallet movements, then applies proprietary scoring algorithms to surface high-conviction alpha opportunities before retail market participants.
Unique: Directly queries Solana RPC mempool and historical transaction data with proprietary whale behavior scoring (accumulation/distribution phase detection) rather than relying on third-party blockchain indexers, enabling sub-second signal freshness and custom threshold tuning per user
vs alternatives: Faster whale detection than Nansen or Arkham Intelligence for Solana because it queries RPC directly instead of waiting for indexed data, and cheaper than subscription-based whale tracking services via pay-per-call USDC model
Analyzes token price action and volume patterns to detect three types of technical divergences: hidden divergence (continuation signals), breakout divergence (reversal setup), and classic divergence (momentum exhaustion). Uses candlestick OHLCV data from Solana DEX to compute RSI/MACD indicators and identify price-momentum misalignments that precede directional moves.
Unique: Implements three distinct divergence detection algorithms (hidden/breakout/classic) with configurable RSI/MACD thresholds, allowing traders to tune sensitivity per market regime rather than using fixed indicator parameters like most TA tools
vs alternatives: More granular divergence classification than TradingView alerts (which only flag classic divergence), and cheaper than hiring human technical analysts to manually scan charts
Scores tokens on a 0-100 scale based on whale accumulation behavior patterns: large wallet inflows, decreasing sell pressure, increasing buy volume concentration. Analyzes on-chain wallet movements and DEX order flow to determine if whales are actively accumulating positions, outputting a quantified accumulation phase score that feeds into trading decision systems.
Unique: Combines multiple on-chain signals (wallet inflows, order book imbalance, sell pressure decay) into a single 0-100 accumulation score with configurable weighting, rather than binary accumulation/not-accumulation classification used by competitors
vs alternatives: More actionable than Glassnode's accumulation metrics because it's Solana-specific and includes real-time DEX order flow, whereas Glassnode focuses on exchange flows and has multi-day lag
Identifies tokens in neutral consolidation phases where whale activity is balanced (neither accumulating nor distributing) and price action is range-bound. Uses on-chain volume distribution, order book depth analysis, and wallet movement patterns to detect equilibrium periods that often precede directional breakouts.
Unique: Detects neutral phases by analyzing order book depth symmetry and whale wallet movement balance rather than just price volatility metrics, enabling detection of equilibrium states even during high-volume consolidations
vs alternatives: More precise than simple Bollinger Band squeeze detection because it incorporates on-chain whale behavior balance, reducing false positives from technical consolidations that lack whale equilibrium
Scores tokens on a 0-100 scale based on whale distribution behavior: large wallet outflows, increasing sell pressure, decreasing buy volume concentration. Analyzes on-chain wallet movements and DEX order flow to determine if whales are actively exiting positions, outputting a quantified distribution phase score that signals when to reduce exposure.
Unique: Combines multiple on-chain exit signals (wallet outflows, order book imbalance, buy pressure decay) into a single 0-100 distribution score with configurable weighting, enabling traders to distinguish between normal profit-taking and aggressive whale exits
vs alternatives: More actionable than on-chain exchange outflow metrics because it detects whale exits directly from DEX order flow rather than waiting for transfers to centralized exchanges, providing 30-60 minutes earlier warning
Analyzes Bitcoin price action, volatility regime, and trend direction to classify the current macro market regime (bull/bear/consolidation) and outputs a regime score that correlates with altcoin performance. Uses BTC OHLCV data, volatility indicators, and trend analysis to provide macro context for Solana token trading decisions.
Unique: Classifies BTC regime using multi-timeframe trend analysis and volatility regime detection rather than simple moving average crossovers, enabling detection of subtle regime transitions and consolidation periods that MA-based systems miss
vs alternatives: More nuanced than simple BTC dominance metrics because it analyzes BTC price action directly and correlates with altcoin performance, whereas dominance only measures market cap allocation
Analyzes Bitcoin futures market data (funding rates, open interest, liquidation levels) to generate trading signals that indicate directional bias and leverage extremes. Detects when futures markets are overextended (high funding rates, extreme open interest) and generates signals for mean-reversion trades or trend continuation based on market structure.
Unique: Combines funding rate extremes, open interest trends, and liquidation cascade detection into a single signal framework rather than analyzing each metric independently, enabling detection of market structure shifts that precede major moves
vs alternatives: More actionable than raw funding rate feeds because it contextualizes funding extremes with open interest and liquidation levels, whereas most traders only watch funding rates in isolation
Integrates CryptoIZ tools into the Model Context Protocol (MCP) ecosystem via Smithery registry, enabling LLM agents and AI applications to call whale intelligence functions directly. Uses x402 Dexter payment gateway to handle USDC micropayments per API call with gas sponsorship, eliminating need for SOL and enabling serverless, pay-as-you-go pricing model.
Unique: Implements MCP server with native x402 Dexter payment integration, enabling gas-sponsored USDC micropayments directly from LLM tool calls without requiring users to manage SOL wallets or gas fees — a novel approach to monetizing AI-native APIs
vs alternatives: Simpler integration than building custom REST API wrappers because MCP tools are natively callable by Claude and other LLMs, and cheaper than traditional SaaS pricing because x402 enables true pay-per-call without subscription overhead
+2 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
cryptoiz-mcp scores higher at 50/100 vs Supabase at 42/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities