| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Queries Chaitin's IP Intelligence API to retrieve comprehensive geolocation data, ASN information, and threat indicators for a given IP address. The tool constructs HTTP requests to Chaitin's REST endpoint, parses JSON responses containing location coordinates, ISP details, and security classifications, and formats results for display. Supports batch lookups through iterative API calls with configurable rate limiting to avoid throttling.
Unique: Direct integration with Chaitin's proprietary IP Intelligence API (Chinese threat intelligence provider), providing access to threat classifications and geolocation data not available through public WHOIS or MaxMind APIs. Implements simple CLI wrapper pattern for rapid IP lookups without requiring complex SDK setup.
vs alternatives: Lighter-weight and faster to deploy than full SIEM platforms, with direct access to Chaitin's threat database; however, limited to Chaitin's intelligence coverage and lacks the multi-source enrichment of commercial platforms like Shodan or AbuseIPDB
Processes multiple IP addresses sequentially through the Chaitin API, aggregating results into a unified output format. The tool reads IP lists from files or stdin, iterates through each address with error handling for invalid IPs, and consolidates responses into structured data (JSON array or CSV table). Implements basic rate-limiting via configurable delays between requests to respect API quotas.
Unique: Implements simple but effective batch aggregation pattern with configurable output formats (JSON, CSV) and built-in rate-limiting delays. Uses streaming file I/O to avoid loading entire IP lists into memory upfront, enabling processing of moderately large datasets without excessive RAM usage.
vs alternatives: Simpler and faster to set up than Splunk or ELK enrichment pipelines, but lacks the distributed processing and fault tolerance of enterprise SIEM batch jobs
Parses JSON responses from Chaitin's API and extracts relevant fields (IP, country, ASN, threat classification, confidence scores) into a normalized data structure. The tool maps API response fields to consistent output schema, handles missing or null values gracefully, and validates data types (e.g., ensuring coordinates are floats, threat levels are enums). Supports multiple output serialization formats (JSON, CSV, human-readable text) from the same parsed data.
Unique: Implements lightweight, schema-aware parsing that normalizes Chaitin's API response format into multiple output formats without requiring a full data transformation framework. Uses Python's native json and csv modules rather than external dependencies, keeping the tool minimal and portable.
vs alternatives: Simpler and faster than building custom Pandas or Polars transformations, but less flexible for complex data transformations or schema evolution
Provides a CLI interface for IP lookups with argument parsing for IP input, output format selection, API key configuration, and rate-limiting parameters. Uses argparse or similar to handle flags like --format (json/csv/text), --output-file, --rate-limit, and --api-key. Supports both interactive prompts and non-interactive scripting modes, with configuration file support for storing API credentials and default parameters.
Unique: Implements a straightforward argparse-based CLI that prioritizes simplicity and shell integration over feature richness. Supports piping and redirection for Unix-style tool composition, allowing IP lookups to be chained with grep, awk, and other command-line utilities.
vs alternatives: More accessible than writing Python scripts directly, but less flexible than a full SDK; comparable to curl-based API wrappers but with better argument handling and output formatting
Handles Chaitin API authentication by accepting and validating API keys, supporting multiple credential input methods (command-line flags, environment variables, configuration files). The tool constructs authenticated HTTP requests by injecting the API key into request headers or query parameters as required by Chaitin's API specification. Implements basic validation to detect missing or invalid credentials before making API calls, reducing wasted requests.
Unique: Implements flexible credential input with support for environment variables and configuration files, allowing secure credential management in containerized and CI/CD environments without hardcoding secrets in code. Uses standard Python os module for environment variable access, avoiding external dependencies.
vs alternatives: More flexible than hardcoded credentials but less secure than dedicated secret management systems like HashiCorp Vault or AWS Secrets Manager; comparable to other CLI tools that support environment variable configuration
Implements error handling for common failure scenarios: invalid IP addresses, API authentication failures, network timeouts, rate limiting (HTTP 429), and malformed API responses. The tool catches exceptions, logs meaningful error messages, and continues processing (for batch operations) or exits gracefully with appropriate exit codes. Supports optional retry logic with exponential backoff for transient failures like network timeouts.
Unique: Implements pragmatic error handling that prioritizes batch job completion over failing fast. Uses try/except blocks to catch API errors and network failures, allowing batch processing to continue even when individual IP lookups fail, with optional error summaries for post-processing analysis.
vs alternatives: More robust than naive implementations that crash on first error, but less sophisticated than enterprise error handling with circuit breakers and adaptive retry strategies
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 Chaitin IP Intelligence at 20/100.
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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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