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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 4 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Scans and analyzes URLs for malicious characteristics by integrating with the urlDNA threat intelligence API through the Model Context Protocol (MCP) interface. The MCP server acts as a bridge between LLM applications and urlDNA's backend scanning engine, allowing language models to invoke URL analysis as a native tool without direct API management. Requests are routed through MCP's standardized tool-calling mechanism, enabling asynchronous threat detection with structured JSON responses containing risk indicators, classification, and metadata.
Unique: Implements URL threat scanning as a native MCP tool, allowing seamless integration into LLM agent workflows without requiring developers to manage API authentication, serialization, or error handling — the MCP server abstracts urlDNA's HTTP API into a standardized tool-calling interface compatible with Claude and other MCP clients
vs alternatives: Provides tighter LLM integration than direct API calls by leveraging MCP's tool-calling protocol, eliminating boilerplate authentication and serialization code while enabling Claude to invoke URL scanning as a first-class capability
Analyzes scanned URLs and returns structured threat classifications (safe, suspicious, malicious) along with confidence scores and risk indicators. The urlDNA backend applies machine learning models and heuristic analysis to categorize URLs based on patterns including domain reputation, SSL certificate validity, content analysis, and known threat databases. Results are returned as JSON objects containing classification labels, numerical risk scores, and detailed threat metadata that can be consumed by downstream LLM reasoning or automated decision-making systems.
Unique: Integrates urlDNA's proprietary threat classification models through MCP, providing LLM agents with structured risk assessments that include confidence scores and threat type indicators — enabling nuanced decision-making beyond binary safe/unsafe verdicts
vs alternatives: Offers more granular threat classification than simple URL blocklists by combining reputation analysis, heuristics, and ML models; stronger than basic domain reputation checks because it analyzes content and behavioral patterns
Registers URL scanning as a callable tool within the MCP protocol, allowing LLM clients (Claude, etc.) to discover and invoke URL analysis through standardized tool-calling mechanisms. The MCP server exposes a tool schema defining input parameters (URL), output structure (threat report), and metadata, enabling the LLM to autonomously decide when to scan URLs based on context. Tool invocation is handled through MCP's request/response protocol, with the server translating tool calls into urlDNA API requests and marshaling responses back to the client.
Unique: Implements MCP tool registration following the Model Context Protocol specification, enabling declarative tool discovery and autonomous invocation by LLMs — the server handles all protocol marshaling, allowing clients to treat URL scanning as a native capability without API management
vs alternatives: Cleaner integration than custom function-calling implementations because it uses standardized MCP tool schema and invocation patterns; more discoverable than direct API integration because the LLM can reason about tool availability and applicability
Processes multiple URLs in sequence or parallel through the MCP interface, coordinating individual URL scans and aggregating threat reports into a consolidated analysis. The implementation likely queues URL scan requests, manages API rate limits, and collects results into a structured batch report. This enables workflows where an LLM agent needs to validate multiple URLs (e.g., from a document, email, or user input) and make decisions based on aggregate threat levels across the batch.
Unique: Orchestrates multiple URL scans through MCP while managing API rate limits and aggregating results into a consolidated threat report — the server abstracts the complexity of batch coordination, allowing LLMs to submit URL lists and receive aggregate threat analysis without managing individual API calls
vs alternatives: More efficient than sequential manual API calls because it handles rate limiting and result aggregation; better than naive parallel scanning because it respects API quotas and prevents rate-limit errors
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 urlDNA at 19/100.
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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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