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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability utilizes a risk scoring algorithm that evaluates various on-chain metrics and historical data to generate a risk score from 0 to 100 for Solana tokens. It incorporates machine learning techniques to identify potential rug pulls by analyzing transaction patterns and user behavior, making it distinct in its predictive accuracy for token safety.
Unique: Employs a machine learning model trained on historical transaction data specific to Solana, enhancing predictive capabilities for risk assessment.
vs alternatives: More accurate than generic risk scoring tools due to its focus on Solana-specific metrics and behaviors.
This capability analyzes buy and sell ratios of Solana tokens to generate actionable momentum signals indicating whether to buy or sell. It leverages real-time market data and applies statistical analysis to determine trends, making it distinct in its ability to provide timely trading recommendations.
Unique: Utilizes a proprietary algorithm that dynamically adjusts to market conditions, providing more relevant signals than static models.
vs alternatives: Faster and more responsive than traditional trading signal generators due to real-time data processing.
This capability allows users to screen up to 10 Solana tokens simultaneously for risk scoring. It employs a parallel processing approach to handle multiple requests efficiently, returning comprehensive risk assessments for each token in a single API call, which is distinct from other tools that only handle one token at a time.
Unique: Optimizes API calls through batch processing, reducing overhead and improving response times compared to sequential requests.
vs alternatives: More efficient than single-token risk assessment tools, saving time for users managing multiple assets.
This capability provides a full analysis of a token, combining both risk scoring and momentum signals into a single output. It integrates multiple data sources and analytical methods to deliver a holistic view of a token's performance, making it distinct in its comprehensive approach to token evaluation.
Unique: Combines multiple analytical frameworks into a single API response, unlike other tools that separate risk and momentum analysis.
vs alternatives: Offers a more integrated view than competitors that provide fragmented insights.
This capability tracks and reports the trading performance of users' investments, calculating win rates and profit/loss (PnL) statistics. It uses historical trade data and integrates with user portfolios to provide real-time performance metrics, making it distinct in its focus on actionable performance insights.
Unique: Integrates seamlessly with user portfolios to provide real-time updates on trading performance, unlike static performance reports.
vs alternatives: More dynamic and user-focused than traditional performance tracking tools that rely on batch updates.
This capability generates live BUY/SKIP decisions based on pump.fun signals, analyzing market trends and user sentiment. It utilizes a combination of sentiment analysis and market data to provide timely recommendations, making it distinct in its responsiveness to market movements.
Unique: Incorporates real-time sentiment analysis from multiple sources to enhance decision-making accuracy, unlike static signal generators.
vs alternatives: More timely and relevant than traditional signal generators that do not account for real-time sentiment.
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.
Solana Token Analysis scores higher at 43/100 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
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