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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables direct HTTPS/SSE connection from modern MCP clients (Claude Desktop 0.48.0+, VS Code Copilot, OpenAI Responses API) to the Wassenger MCP server at https://api.wassenger.com/mcp without local installation. API key is embedded as URL query parameter and validated on every request, eliminating OAuth complexity for stateless clients. Uses Server-Sent Events (SSE) for bidirectional message streaming over standard HTTPS.
Unique: Implements stateless HTTP streaming transport with query-parameter API key validation, eliminating the need for local OAuth flows or proxy servers for modern MCP clients. Uses standard HTTPS/SSE rather than custom protocols, enabling deployment on any CDN or reverse proxy.
vs alternatives: Faster onboarding than NPX package setup (no Node.js installation) and more secure than embedding credentials in client config files, though less suitable for sensitive environments than OAuth-based alternatives.
Provides a local Node.js proxy (mcp-wassenger NPX package) that bridges STDIO transport from legacy MCP clients (Cline, Continue) to SSE connection with the Wassenger API. Implements OAuth 2.0 with PKCE flow for secure credential handling, using lockfile-based coordination (src/lib/coordination.ts) to prevent duplicate browser authentication flows when multiple client instances spawn the proxy simultaneously. Parses command-line arguments and manages credential lifecycle.
Unique: Implements lockfile-based multi-instance coordination (src/lib/coordination.ts 12-143) to prevent duplicate OAuth browser flows when multiple client processes spawn the proxy concurrently. Uses PKCE (Proof Key for Code Exchange) for secure OAuth without client secrets in memory, and bridges STDIO ↔ SSE bidirectionally via MCP SDK transports.
vs alternatives: More secure than HTTP streaming's query-parameter API keys (uses OAuth tokens with expiration) and supports legacy clients, but requires local installation and adds startup latency vs direct HTTP streaming.
Automatically generates MCP tool schemas (JSON Schema format) for all Wassenger API operations, with built-in type validation and parameter documentation. Implements schema validation on tool invocation to catch missing or invalid parameters before sending to Wassenger API. Provides TypeScript type definitions for all tool inputs/outputs, enabling IDE autocomplete and compile-time type checking.
Unique: Generates MCP tool schemas with embedded TypeScript type definitions, enabling compile-time type checking and IDE autocomplete for Wassenger operations. Implements client-side parameter validation to catch errors before API calls.
vs alternatives: More developer-friendly than raw JSON schemas (TypeScript types + autocomplete) and more reliable than runtime-only validation, though less flexible than dynamic schema generation from live API introspection.
Implements comprehensive error handling for Wassenger API failures, including network errors, rate limiting (HTTP 429), and API errors (HTTP 4xx/5xx). Provides automatic retry logic with exponential backoff (initial delay 100ms, max delay 30s) for transient failures, with configurable retry counts. Returns detailed error messages to AI clients, distinguishing between retryable errors (rate limit, timeout) and permanent failures (invalid parameters, authentication).
Unique: Implements exponential backoff retry logic with configurable retry counts and distinguishes between retryable errors (rate limit, timeout) and permanent failures (invalid parameters). Provides detailed error metadata to clients for intelligent error handling.
vs alternatives: More resilient than single-attempt API calls and more transparent than silent retries (returns detailed error info), though less sophisticated than circuit breaker patterns for cascading failure prevention.
Provides flexible configuration loading from environment variables, command-line arguments, and configuration files (.env, JSON). Implements secure credential storage with support for API keys, OAuth secrets, and webhook URLs. Validates configuration on startup and provides helpful error messages for missing or invalid settings. Supports configuration inheritance and overrides (CLI args > env vars > config files).
Unique: Implements configuration loading with priority order (CLI args > env vars > config files) and validates all settings on startup, providing helpful error messages for missing or invalid configurations. Supports both .env files and JSON configuration files.
vs alternatives: More flexible than hardcoded configuration and more accessible than external secret management services (Vault, AWS Secrets Manager), though less secure than encrypted secret storage and requires manual credential rotation.
Enables AI clients to send text messages to WhatsApp contacts or groups via the Wassenger API, with built-in recipient validation (phone number format checking, contact existence verification) and delivery status tracking. Implements message queuing to handle rate limits (typically 80 messages/minute per Wassenger account) and provides structured responses indicating success, pending, or failure states. Supports both individual chats and group messaging with automatic recipient type detection.
Unique: Integrates recipient validation and delivery status tracking directly into the MCP tool interface, allowing AI clients to handle failures and retries without external polling. Implements client-side rate limit awareness to prevent API quota exhaustion during batch operations.
vs alternatives: More integrated than raw Wassenger API calls (validation + status tracking built-in) and more reliable than webhook-based delivery tracking (synchronous responses), though less feature-rich than Twilio's WhatsApp API for complex media handling.
Provides AI clients with the ability to fetch and analyze WhatsApp conversation history from specific chats or groups, extracting message content, sender metadata, timestamps, and media references. Implements pagination for large conversations (typically 50-100 messages per page) and optional filtering by date range or sender. Returns structured conversation data suitable for RAG (Retrieval-Augmented Generation) pipelines or conversation analysis tasks.
Unique: Exposes conversation history as structured MCP tools with built-in pagination and filtering, enabling AI clients to fetch context on-demand without managing separate API calls or database queries. Integrates directly with LLM context windows for immediate use in prompts.
vs alternatives: More accessible than raw Wassenger API (pagination + filtering built-in) and more real-time than webhook-based conversation logging, though less feature-rich than dedicated conversation analytics platforms like Intercom for advanced segmentation.
Enables AI clients to create WhatsApp groups, add/remove members, update group metadata (name, description, icon), and manage group permissions. Implements role-based access control (admin vs member) and provides group listing with member counts and metadata. Supports bulk member operations with error handling for invalid phone numbers or permission violations.
Unique: Provides group management as atomic MCP tools with built-in error handling for permission violations and invalid members, allowing AI clients to orchestrate group operations without managing WhatsApp API complexity. Supports bulk member operations with partial success reporting.
vs alternatives: More integrated than raw Wassenger API (error handling + bulk operations built-in) and more accessible than WhatsApp Business API direct integration (no webhook management required), though less feature-rich than dedicated group management platforms for advanced analytics.
+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 Wassenger 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