| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes penetration testing utilities and security scanning capabilities through the Model Context Protocol (MCP) server interface, allowing Claude and other MCP-compatible clients to invoke security tools via standardized resource and tool definitions. Implements MCP server lifecycle management with stdio transport, enabling bidirectional communication between LLM clients and pentest-specific functionality without custom API wrappers.
Unique: Bridges penetration testing tools directly into Claude's context via MCP protocol, eliminating the need for custom API wrappers or shell scripting to invoke security tools from LLM conversations
vs alternatives: Provides native MCP integration for pentest tools where alternatives require manual tool invocation or custom scripting, enabling seamless LLM-driven security workflows
Collects and aggregates reconnaissance data (DNS records, WHOIS information, port scans, service enumeration) from multiple sources and presents it through MCP resources, allowing Claude to access comprehensive target intelligence in a structured format. Likely implements wrapper functions around standard reconnaissance tools (nmap, dig, whois) with output normalization and caching.
Unique: Aggregates multiple reconnaissance sources (DNS, WHOIS, port scanning) into unified MCP resources, allowing Claude to access complete target intelligence without invoking individual tools sequentially
vs alternatives: Faster reconnaissance workflow than manually running separate tools and parsing outputs, with structured data presentation optimized for LLM consumption
Provides vulnerability scanning capabilities (likely wrapping tools like Nessus, OpenVAS, or Metasploit) and generates exploitation guidance based on discovered vulnerabilities. Implements tool invocation with result parsing and risk assessment, presenting findings through MCP resources that Claude can analyze and recommend exploitation paths for.
Unique: Combines vulnerability scanning with LLM-driven exploitation guidance generation, allowing Claude to not just identify vulnerabilities but recommend specific exploitation approaches based on discovered weaknesses
vs alternatives: Integrates vulnerability discovery with exploitation planning in a single workflow, whereas traditional tools require manual analysis and separate exploitation frameworks
Orchestrates payload generation (shellcode, reverse shells, web shells) and delivery mechanisms through MCP tool definitions, allowing Claude to request specific payloads and coordinate delivery across multiple attack vectors. Likely implements templates for common payloads (Metasploit integration, custom shellcode generation) with encoding/obfuscation options.
Unique: Integrates payload generation with LLM-driven orchestration, allowing Claude to request context-aware payloads and coordinate multi-stage delivery without manual tool invocation
vs alternatives: Streamlines payload generation and delivery coordination compared to manual Metasploit usage, with LLM-driven decision-making for payload selection and encoding strategies
Provides post-exploitation capabilities including remote command execution, privilege escalation guidance, and persistence mechanism deployment through MCP tool definitions. Implements command execution wrappers (likely SSH, WinRM, or reverse shell integration) with output capture and analysis, allowing Claude to execute commands on compromised systems and recommend persistence techniques.
Unique: Integrates post-exploitation command execution with LLM-driven decision-making, allowing Claude to execute commands and recommend persistence strategies based on target system analysis
vs alternatives: Enables interactive post-exploitation workflows through Claude conversation rather than manual shell interaction, with LLM-driven privilege escalation and persistence recommendations
Orchestrates lateral movement techniques (credential harvesting, network reconnaissance from compromised hosts, pivot chain setup) through MCP tools, allowing Claude to plan and execute multi-hop attack chains across network segments. Implements network mapping from compromised systems and coordinates pivot infrastructure setup.
Unique: Coordinates multi-hop lateral movement planning through LLM-driven analysis, allowing Claude to recommend optimal pivot paths based on network topology and credential availability
vs alternatives: Automates lateral movement planning and coordination compared to manual pivot setup, with LLM-driven decision-making for path selection and infrastructure configuration
Provides data exfiltration planning and execution capabilities through MCP tools, allowing Claude to identify valuable data, plan exfiltration methods, and coordinate data collection from compromised systems. Implements data discovery (file enumeration, database queries) and exfiltration method selection (DNS tunneling, HTTPS, steganography) with output formatting.
Unique: Integrates data discovery and exfiltration planning with LLM-driven analysis, allowing Claude to identify valuable data and recommend evasion-aware exfiltration methods
vs alternatives: Automates data discovery and exfiltration planning compared to manual enumeration, with LLM-driven prioritization and method selection based on target environment analysis
Provides guidance on evading security tools (antivirus, EDR, IDS/IPS, WAF) through MCP resources, analyzing target security posture and recommending evasion techniques. Implements detection signature analysis, behavioral evasion recommendations, and obfuscation strategy selection based on identified security controls.
Unique: Provides LLM-driven evasion guidance based on identified security tools, allowing Claude to recommend context-aware evasion strategies rather than generic techniques
vs alternatives: Tailors evasion recommendations to specific target security posture compared to generic evasion guides, with LLM-driven analysis of tool-specific detection mechanisms
+2 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 pentest-copilot at 26/100.
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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