| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Connects to live MCP servers using the MCPScanner class, retrieves tool/prompt/resource descriptions and configurations, and submits natural-language content to the Invariant analysis API for vulnerability detection. Uses a two-stage pipeline: MCP client layer establishes connections and enumerates server capabilities, then the analysis pipeline extracts and redacts sensitive data before remote submission for LLM-based threat detection.
Unique: Targets natural-language attack vectors (prompt injection, tool poisoning, toxic flows) specific to MCP infrastructure by analyzing tool descriptions and configurations rather than code; integrates with Invariant API for LLM-based semantic threat detection rather than pattern matching
vs alternatives: Detects MCP-specific supply chain attacks (cross-origin toxic flows) that generic SAST tools miss because it understands agent workflow semantics and tool composition patterns
Injects the Invariant Gateway into MCP client configurations to intercept live MCP traffic at runtime without modifying agent code. The proxy command rewrites client configuration files to route all MCP calls through a FastAPI-based mcp_scan_server that validates requests/responses against security policies before forwarding to actual MCP servers. Implements real-time policy enforcement with session-based state tracking and configurable guardrails.
Unique: Implements transparent MCP traffic interception via configuration rewriting rather than code instrumentation; uses session-based state tracking to enforce stateful policies (e.g., preventing toxic tool chains across multiple calls) and integrates Invariant Gateway for real-time semantic validation
vs alternatives: Provides runtime guardrailing without modifying agent code or MCP server implementations, enabling security policies to be deployed and updated independently of application releases
Maintains session-based state for MCP interactions in proxy mode, tracking tool calls, responses, and policy decisions across multiple requests. Stores session state in memory or external persistence layer (Redis, database) and generates comprehensive audit logs of all MCP activity. Enables stateful policy enforcement (e.g., preventing toxic tool chains) and compliance auditing.
Unique: Implements session-based state tracking with support for both in-memory and external persistence; enables stateful policy enforcement and comprehensive audit logging for compliance and incident investigation
vs alternatives: Provides built-in session state management and audit logging without requiring external logging infrastructure, enabling stateful policies and compliance auditing within the proxy
Captures and logs all MCP traffic (requests, responses, errors) for debugging and analysis. Provides detailed logging of MCP client-server interactions including payloads, timing, and error details. Supports traffic export in multiple formats (JSON, HAR) for analysis in external tools. Enables troubleshooting of MCP connectivity issues and understanding of agent behavior.
Unique: Implements comprehensive traffic capture with support for multiple export formats (JSON, HAR) and detailed timing/error information; integrates with proxy mode for transparent traffic logging without code changes
vs alternatives: Provides built-in traffic capture and debugging without requiring external packet capture tools, enabling easy analysis of MCP interactions within the scanning framework
Parses and validates MCP configuration files in JSON and YAML formats, extracting server definitions, authentication credentials, and transport protocol specifications. Validates configuration syntax and schema, detects missing required fields, and provides detailed error messages for invalid configurations. Supports multiple configuration file formats and locations (environment variables, default paths).
Unique: Implements schema-based validation for MCP configuration files with detailed error messages and support for multiple formats (JSON, YAML); integrates with configuration discovery to support multiple configuration sources
vs alternatives: Provides built-in configuration validation without requiring external schema validation tools, enabling early detection of configuration errors in CI/CD pipelines
Scans AI agent skills (packaged agent components) for embedded malware payloads, sensitive data handling violations, exposure to untrusted third parties, and hard-coded secrets using static analysis and pattern matching. Analyzes skill code, dependencies, and metadata to identify security risks before skills are integrated into agent systems. Supports both direct skill file scanning and skill registry lookups.
Unique: Combines static code analysis, signature-based malware detection, and dependency auditing specifically for agent skills; integrates with Snyk vulnerability database for known CVEs and provides skill-specific risk scoring beyond generic SAST
vs alternatives: Detects agent skill-specific risks (untrusted third-party access, sensitive data handling in skill context) that generic dependency scanners miss by understanding agent execution models and data flow patterns
Provides an offline inspect command that analyzes MCP servers and agent components locally without submitting data to remote APIs. Uses local pattern matching, heuristic analysis, and built-in vulnerability signatures to detect common security issues. Enables security-sensitive organizations to scan infrastructure without external network calls while maintaining privacy of tool descriptions and configurations.
Unique: Implements local-first vulnerability detection using built-in heuristics and pattern signatures, enabling offline scanning without external API dependencies; trades detection accuracy for privacy and network isolation
vs alternatives: Enables security scanning in restricted environments where remote API calls are prohibited, while maintaining the same CLI interface as remote scanning for operational consistency
Implements automatic data redaction in the scan analysis pipeline to remove or mask sensitive information (credentials, PII, proprietary details) before submitting tool descriptions and configurations to the Invariant analysis API. Uses configurable redaction rules and pattern matching to identify and redact secrets, API keys, email addresses, and other sensitive data. Maintains a redaction audit trail for compliance and debugging.
Unique: Integrates redaction as a first-class pipeline stage before remote submission, using configurable pattern-based rules and maintaining audit trails; enables privacy-preserving analysis without requiring separate data sanitization tools
vs alternatives: Provides built-in privacy controls within the scanning pipeline rather than requiring external data masking tools, reducing operational complexity and ensuring consistent redaction across all scan types
+5 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 agent-scan at 37/100.
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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