Supabase ranks higher at 42/100 vs Token Holder Analysis — Distribution & Whale Detection at 31/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Token Holder Analysis — Distribution & Whale Detection | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 31/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 4 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability analyzes the distribution of holders for any ERC-20 token by aggregating data from the blockchain to identify top holders, their percentage ownership, and the overall distribution structure. It utilizes a combination of on-chain data retrieval and statistical analysis to compute metrics such as the Gini concentration coefficient and whale counts, providing insights into the token's decentralization and potential risks. The integration with the x402 micropayment system allows for seamless access without requiring an API key, making it user-friendly for quick analyses.
Unique: Utilizes a micropayment model for access, allowing for low-cost, on-demand analysis without the need for API keys, which is uncommon in blockchain analytics tools.
vs alternatives: More cost-effective and accessible than traditional token analytics platforms that require subscriptions or API keys.
This capability identifies and counts the number of 'whales'—addresses holding a significant percentage of the total token supply—by analyzing the distribution of token holdings. It employs threshold-based logic to classify addresses as whales based on their ownership percentage, providing users with insights into potential market manipulation risks. The results are returned in a structured format, allowing for easy integration into other applications or analyses.
Unique: Offers a customizable whale definition based on user-defined thresholds, allowing for tailored risk assessments rather than a one-size-fits-all approach.
vs alternatives: More flexible in whale classification compared to static models used by other analytics tools.
This capability calculates the Gini concentration coefficient for the token's holder distribution, providing a quantitative measure of inequality among holders. It processes the distribution data to derive the coefficient, which indicates how concentrated the token ownership is. A higher Gini coefficient suggests greater inequality, which can signal potential risks for investors. The implementation leverages statistical formulas to ensure accuracy and reliability in the results.
Unique: Calculates the Gini coefficient specifically for ERC-20 tokens, providing a tailored metric that is not commonly available in standard token analysis tools.
vs alternatives: More focused on inequality measurement compared to general analytics platforms that may overlook this metric.
This capability assesses the trend of token holders over time, indicating whether the number of holders is growing or shrinking. It analyzes historical data to identify patterns in holder behavior, which can be crucial for understanding market sentiment and potential future price movements. The implementation involves tracking changes in holder counts and applying trend analysis algorithms to provide clear insights.
Unique: Provides a dynamic view of holder trends over time, which is often overlooked in static analyses of token distributions.
vs alternatives: More focused on temporal analysis compared to competitors that only provide snapshot data.
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 Token Holder Analysis — Distribution & Whale Detection at 31/100.
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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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