Supabase ranks higher at 42/100 vs Rug Munch Intelligence — MCP Server at 32/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Rug Munch Intelligence — MCP Server | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 32/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability evaluates the risk associated with a cryptocurrency token by providing a quick risk score from 0 to 100, along with a recommendation. It utilizes a combination of on-chain data analysis and social media sentiment analysis to generate the score, allowing users to make informed decisions before transacting. The architecture leverages a microservices approach, where the risk assessment is performed in real-time through API calls to the Rug Munch Intelligence backend.
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 alternatives: More comprehensive than basic token analysis tools as it combines multiple data sources for risk evaluation.
This capability allows users to assess the risk of up to 20 tokens simultaneously, providing a batch risk score and recommendations for each token. It employs efficient API calls to process multiple requests in parallel, reducing the time needed for evaluations. The architecture is designed to handle bulk requests seamlessly, utilizing asynchronous processing to enhance performance.
Unique: Utilizes asynchronous API calls to efficiently handle multiple token evaluations in a single request, unlike many tools that process tokens sequentially.
vs alternatives: Faster than competitors by processing batch requests concurrently, reducing overall evaluation time.
This capability analyzes the history of a token's deployer wallet to identify patterns of behavior, such as whether the deployer has a history of rug pulls. It employs a combination of on-chain transaction analysis and historical data mining to assess the deployer's credibility. The analysis is performed through dedicated API endpoints that aggregate and analyze wallet activity over time.
Unique: Focuses specifically on deployer wallet behavior, providing insights that are often overlooked by standard token analysis tools.
vs alternatives: More thorough than traditional tools by providing historical context on deployers, which is crucial for risk assessment.
This capability retrieves and analyzes social media presence and red flags associated with a token, providing insights into community sentiment and potential risks. It leverages APIs to gather data from various social media platforms and applies natural language processing to identify negative sentiment or warnings. The architecture allows for real-time data collection and analysis, ensuring timely insights.
Unique: Combines social media sentiment analysis with token evaluation, offering a unique perspective on community perceptions that is often absent in traditional analysis.
vs alternatives: Provides a more holistic view of token risks by integrating social sentiment, unlike standard risk assessment tools.
This capability detects patterns of coordinated buying activity for a token, which can indicate potential manipulation or pump-and-dump schemes. It analyzes transaction data to identify unusual spikes in buying activity and correlates them with wallet addresses. The implementation uses advanced statistical methods to flag suspicious patterns, providing users with alerts on potential risks.
Unique: Employs statistical analysis to identify coordinated buying patterns, providing insights that are often missed by standard transaction monitoring tools.
vs alternatives: More sophisticated than basic transaction analysis tools by focusing on behavioral patterns indicative of market manipulation.
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 Rug Munch Intelligence — MCP Server at 32/100.
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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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