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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Coordinates 11 specialized vulnerability detection scanners through the MCPScanner orchestrator class using a pipeline pattern that manages repository cloning, parallel scanner execution, result aggregation, and cleanup operations. Each scanner extends an AbstractScanner base class providing common utilities for credential sanitization, file system operations, and result formatting, enabling modular vulnerability detection across MCP server implementations.
Unique: Implements a modular scanner architecture with 11 research-backed vulnerability detectors coordinated through a single orchestrator class, enabling extensible security scanning specific to MCP protocol implementations rather than generic code analysis
vs alternatives: Purpose-built for MCP security with domain-specific vulnerability patterns from VulnerableMCP database and HiddenLayer research, whereas generic SAST tools lack MCP protocol-specific detection rules
Implements CredentialScanner that detects hardcoded API keys, tokens, and insecure credential storage patterns in MCP server code using pattern matching against known credential formats (AWS keys, OpenAI tokens, private keys, etc.). The scanner includes built-in credential sanitization utilities in the AbstractScanner base class to mask sensitive data in reports, preventing accidental exposure of discovered secrets.
Unique: Combines credential pattern detection with built-in sanitization utilities in the AbstractScanner base class, ensuring discovered secrets are masked in reports to prevent secondary exposure when sharing vulnerability findings
vs alternatives: Integrated sanitization prevents accidental secret leakage in reports unlike generic secret scanners (git-secrets, TruffleHog) which may expose raw credentials in output
Executes all 11 vulnerability scanners in parallel using asynchronous operations, aggregating results from each scanner into a unified report. The orchestrator manages concurrent execution to balance performance with resource utilization, collecting vulnerability objects from each scanner and merging them by category and severity for comprehensive reporting.
Unique: Implements parallel scanner execution in the MCPScanner orchestrator with result aggregation, enabling all 11 vulnerability detectors to run concurrently while merging results into a unified report
vs alternatives: Concurrent execution versus sequential scanning reduces total scan time by leveraging multiple CPU cores, improving performance for large codebases
Provides AbstractScanner base class with shared utilities including credential sanitization, file system operations, result formatting, and error handling. All specialized scanners extend this base class to inherit common functionality, reducing code duplication and ensuring consistent vulnerability reporting across all scanner implementations. Utilities include regex-based pattern matching, file reading, and credential masking.
Unique: Provides AbstractScanner base class with built-in credential sanitization, file operations, and result formatting utilities, enabling consistent vulnerability reporting and reducing code duplication across all 11 specialized scanners
vs alternatives: Shared base class utilities versus duplicated code in each scanner, improving maintainability and consistency
Implements ToolPoisoningScanner that detects hidden malicious code, suspicious function implementations, and tool poisoning attacks in MCP server tool definitions. The scanner analyzes function signatures, implementation patterns, and data flow to identify code that may exfiltrate data, execute arbitrary commands, or bypass security controls through the MCP tool interface.
Unique: Analyzes MCP-specific tool definitions and function implementations to detect poisoning attacks targeting the tool interface, using data flow analysis to identify suspicious exfiltration or command execution patterns unique to MCP protocol
vs alternatives: MCP-specific tool poisoning detection versus generic code analysis tools that lack understanding of MCP tool semantics and attack vectors
Implements scanners that detect parameter injection vulnerabilities, improper input validation, and MCP protocol violations in server implementations. The detection engine analyzes how MCP servers handle tool parameters, resource requests, and protocol messages to identify injection attack vectors, missing validation, and deviations from the MCP specification that could enable exploitation.
Unique: Combines parameter injection detection with MCP protocol compliance validation, analyzing both input handling security and adherence to the MCP specification to identify vulnerabilities specific to the protocol implementation
vs alternatives: Protocol-aware injection detection versus generic SAST tools that lack MCP-specific validation rules and protocol compliance checks
Integrates vulnerability detection patterns derived from authoritative security research sources including the VulnerableMCP database, HiddenLayer research on parameter injection attacks, and Trail of Bits credential security analysis. The system maps research findings to specialized scanner implementations, enabling detection of known MCP vulnerability categories with patterns informed by real-world attack research and security best practices.
Unique: Explicitly integrates multiple authoritative security research sources (VulnerableMCP database, HiddenLayer, Trail of Bits) into scanner implementations, providing research-backed vulnerability detection with source attribution rather than heuristic-only pattern matching
vs alternatives: Research-informed vulnerability detection with explicit source attribution versus generic security scanners that lack MCP-specific threat intelligence and research integration
Implements configurable severity filtering (critical, high, medium, low) and vulnerability category filtering that allows users to focus scan results on relevant threats. The reporting system aggregates vulnerabilities by category and severity, providing both detailed findings and summary statistics. Users can filter results before or after scanning to customize output based on risk tolerance and compliance requirements.
Unique: Provides both pre-scan category filtering and post-scan severity filtering with aggregated summary statistics, enabling flexible result customization for different stakeholder needs and compliance requirements
vs alternatives: Integrated filtering and aggregation within the scanner versus separate post-processing tools, reducing friction for developers and security teams
+4 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 MCPWatch at 27/100.
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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