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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Generates trading signals by analyzing cryptocurrency market data through quantitative models and technical indicators. The MCP server exposes signal generation as a tool that Claude and other LLM clients can invoke, passing market parameters and receiving structured signal outputs (buy/sell/hold recommendations with confidence scores). Integrates with crypto data providers to fetch OHLCV data, compute indicators (RSI, MACD, Bollinger Bands, etc.), and apply statistical models to produce actionable trading signals.
Unique: Exposes quantitative signal generation as an MCP tool callable by Claude and other LLMs, enabling natural language-driven crypto analysis workflows where agents can request signals, interpret them, and make trading decisions within a single conversation context. Uses MCP's tool-calling protocol to abstract away signal computation details while maintaining full parameter control.
vs alternatives: Unlike standalone crypto APIs or trading bots, this MCP server integrates signal generation directly into LLM reasoning loops, allowing Claude to combine quantitative signals with qualitative analysis, risk assessment, and multi-asset correlation in a single agentic workflow.
Computes technical indicators (RSI, MACD, Bollinger Bands, moving averages, etc.) across multiple timeframes simultaneously and aggregates results into a unified signal consensus. The server accepts a cryptocurrency symbol and returns indicator values for 1m, 5m, 15m, 1h, 4h, 1d timeframes in a single call, enabling multi-timeframe analysis patterns where short-term and long-term trends are evaluated together. Aggregation logic may apply voting or weighting schemes to produce a composite signal strength.
Unique: Bundles multi-timeframe indicator computation into a single MCP tool call, reducing round-trip latency and API quota consumption compared to fetching each timeframe separately. Implements aggregation logic (consensus voting, weighted scoring) server-side, allowing Claude to reason about trend alignment without manual cross-timeframe comparison.
vs alternatives: Faster and simpler than calling separate indicator APIs for each timeframe; provides built-in consensus logic that LLM agents can directly interpret, whereas generic charting APIs require the client to implement aggregation logic.
Implements comprehensive error handling and input validation for MCP protocol interactions, including parameter schema validation, graceful error responses, and detailed error messages. The server validates all inputs against JSON Schema definitions and returns structured error responses with actionable guidance.
Unique: Implements MCP-specific error handling with JSON Schema validation and structured error responses. Provides detailed validation errors that help clients understand and fix invalid requests.
vs alternatives: More robust than unvalidated APIs and provides better developer experience than generic HTTP error codes.
Validates signal generation logic against historical cryptocurrency price data by replaying signals across past market conditions and computing performance metrics (win rate, profit factor, max drawdown, Sharpe ratio). The server accepts a signal strategy definition (indicator parameters, entry/exit rules) and a date range, then simulates trades on historical OHLCV data and returns detailed backtest results. Enables rapid iteration on signal parameters without live trading risk.
Unique: Integrates backtesting as an MCP tool, allowing Claude to propose signal strategies, validate them against historical data, and iterate on parameters within a single conversation. Computes standard quant metrics (Sharpe ratio, max drawdown, profit factor) server-side, enabling LLM agents to reason about strategy quality without manual calculation.
vs alternatives: More accessible than standalone backtesting frameworks (Backtrader, VectorBT) because it's callable from Claude without coding; provides structured output that LLMs can interpret and reason about, whereas traditional backtesting tools require manual result interpretation.
Connects to cryptocurrency exchange WebSocket APIs (Binance, Kraken, Coinbase, etc.) to stream real-time OHLCV data, order book updates, and trade ticks. The MCP server maintains persistent WebSocket connections and exposes current market state as queryable tools, allowing Claude to request the latest price, volume, bid-ask spread, or order book depth for any crypto asset. Handles reconnection logic, data buffering, and timestamp synchronization automatically.
Unique: Abstracts WebSocket connection management and multi-exchange data normalization behind MCP tools, allowing Claude to query real-time market data without handling connection lifecycle or format differences. Maintains persistent connections server-side, reducing latency compared to opening new connections per query.
vs alternatives: Simpler than direct exchange API integration because MCP handles connection pooling and reconnection; faster than polling REST APIs because it uses push-based WebSocket updates; more accessible than building custom data pipelines because it's a single MCP tool call.
Analyzes price correlations and technical indicator divergences across multiple cryptocurrency assets to identify trading opportunities. Computes rolling correlations between asset pairs (e.g., BTC vs ETH, BTC vs altcoins), detects when correlated assets diverge from expected relationships, and identifies bullish/bearish divergences (price makes new high but indicator does not). Returns structured divergence alerts with asset pairs, divergence type, and confidence scores.
Unique: Computes correlation matrices and divergence detection across multiple assets server-side, exposing results as structured MCP tools that Claude can query and reason about. Detects both price-indicator divergences and cross-asset correlation breaks in a single call, reducing the need for multiple analysis steps.
vs alternatives: More efficient than manually comparing multiple assets and indicators; provides structured divergence data that LLMs can interpret directly; faster than building custom correlation analysis because it's pre-built and optimized for crypto markets.
Analyzes historical volatility patterns to detect current market regime (low volatility, high volatility, volatility expansion, volatility contraction) and forecasts near-term volatility using models like GARCH or exponential weighted moving average (EWMA). Returns current volatility regime classification, volatility percentile (current vol vs historical range), and volatility forecast for next N candles. Enables regime-aware signal filtering and position sizing.
Unique: Combines volatility regime detection with forecasting in a single MCP tool, allowing Claude to query both current market conditions and near-term volatility expectations. Uses GARCH or EWMA models server-side to compute forecasts, enabling LLM agents to make volatility-aware decisions without implementing statistical models.
vs alternatives: More accessible than standalone volatility modeling libraries (arch, statsmodels) because it's a single MCP call; provides regime classification that LLMs can directly interpret, whereas raw volatility numbers require manual interpretation.
Allows users to define custom alert rules (e.g., 'alert when BTC price crosses above 50-day MA', 'alert when RSI > 70 on 1h timeframe', 'alert when correlation between BTC and ETH drops below 0.7') and automatically monitors market conditions to trigger notifications. The MCP server maintains rule state, evaluates conditions against real-time or periodic market data, and exposes tools to create, update, delete, and list active alerts. Supports multiple notification channels (webhook, email, Slack, Discord, etc.).
Unique: Exposes alert management as MCP tools, allowing Claude to create, update, and manage trading alerts conversationally. Integrates with multiple notification channels (webhook, Slack, Discord, email) and maintains alert state server-side, enabling persistent monitoring without client-side polling.
vs alternatives: More flexible than exchange-native alerts because it supports custom conditions (technical indicators, correlations, divergences); more accessible than building custom monitoring systems because alert logic is defined through MCP tools rather than code.
+3 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.
Supabase scores higher at 42/100 vs crypto-quant-signal-mcp at 36/100.
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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