| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the ModelContextProtocol server specification to establish bidirectional communication with MCP clients (Claude, other LLM applications). Handles protocol version negotiation, capability advertisement, and message routing through stdio or HTTP transports. Uses JSON-RPC 2.0 message framing to serialize tool definitions and responses between client and server.
Unique: Implements full MCP server specification with stdio transport, enabling native integration with Claude and other MCP clients without requiring custom API wrappers or authentication layers
vs alternatives: Simpler than building REST APIs + custom Claude plugins because it uses standardized MCP protocol that Claude natively understands
Exposes email campaign CRUD operations as MCP tools that LLM clients can invoke. Implements schema-based function definitions for creating campaigns with parameters like subject, body, recipient lists, and scheduling. Routes tool calls to underlying marketing platform APIs (likely Bolide's own backend or third-party services like Mailchimp/SendGrid) and returns structured campaign metadata and status.
Unique: Wraps email campaign operations as MCP tools with schema validation, allowing Claude to understand campaign parameters and constraints before execution, reducing malformed requests compared to unstructured API calls
vs alternatives: More natural than Zapier/Make automations because Claude can reason about campaign content and recipient targeting in real-time rather than following rigid workflow rules
Provides MCP tools for querying, filtering, and segmenting contact databases based on attributes (demographics, engagement history, purchase behavior). Implements parameterized filtering logic that translates natural language intent (e.g., 'high-value customers who opened emails in the last 30 days') into database queries. Returns segment metadata including size, engagement metrics, and preview samples.
Unique: Translates natural language audience descriptions into parameterized database queries with schema validation, enabling Claude to suggest segments without exposing raw SQL or requiring manual filter configuration
vs alternatives: More flexible than static audience lists because Claude can dynamically compose segments based on conversation context and user feedback in real-time
Extends campaign automation to SMS and push notification channels via MCP tools. Implements channel-specific schema definitions (SMS character limits, push notification title/body constraints) and routes messages through appropriate service providers (Twilio, Firebase, etc.). Handles delivery tracking, bounce management, and opt-out compliance per channel.
Unique: Enforces channel-specific constraints (SMS character limits, push notification field lengths) at the tool schema level, preventing Claude from generating invalid messages before execution
vs alternatives: More integrated than managing SMS and push separately because a single MCP server handles all channels with unified campaign metadata and tracking
Provides MCP tools for querying campaign metrics (open rates, click rates, conversion rates, revenue attribution) and generating reports. Implements aggregation logic that translates natural language queries ('Which campaigns had the highest ROI last month?') into analytics queries. Returns structured metrics with time-series data, comparisons, and trend analysis.
Unique: Translates conversational analytics queries into structured metric requests with automatic time-series aggregation and comparison logic, enabling Claude to answer 'Which campaigns performed best?' without manual SQL or dashboard navigation
vs alternatives: More accessible than BI tools like Tableau because Claude can interpret business questions and fetch relevant metrics without requiring users to understand data schemas or write queries
Provides MCP tools for storing, retrieving, and managing email/SMS/push templates. Implements template variable substitution (e.g., {{first_name}}, {{discount_code}}) with validation to ensure all required variables are provided at send time. Integrates with Claude's text generation to help draft template content and suggest personalization variables based on available contact attributes.
Unique: Validates template variables at save time and provides Claude with available contact attributes, enabling intelligent template suggestions that match actual data in the contact database
vs alternatives: More intelligent than static template libraries because Claude can suggest personalization variables based on contact schema and help draft content that leverages available data
Provides MCP tools for defining event-triggered campaigns (e.g., 'send email when contact signs up', 'send SMS when purchase exceeds $100'). Implements trigger schema with event types, conditions, and action definitions. Routes trigger configurations to a workflow engine that listens for events and executes associated campaigns automatically. Supports complex conditions (AND/OR logic, time windows) and action chaining.
Unique: Exposes trigger configuration as MCP tools with schema validation for conditions and actions, allowing Claude to suggest trigger logic based on business context and validate conditions before deployment
vs alternatives: More flexible than no-code automation builders because Claude can reason about trigger logic and suggest optimizations based on campaign performance data
Provides MCP tools for importing contacts from external sources (CSV, API, CRM) and syncing contact data with upstream systems. Implements field mapping logic to translate external data schemas to internal contact model. Handles deduplication, validation, and conflict resolution (e.g., which system wins if email exists in both sources). Supports incremental syncs and batch imports with progress tracking.
Unique: Provides field mapping tools with schema validation and deduplication logic, allowing Claude to suggest optimal mappings based on data preview and validate imports before execution
vs alternatives: More reliable than manual CSV imports because it enforces field validation and deduplication rules, reducing duplicate contacts and data quality issues
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 @bolide-ai/mcp at 23/100. @bolide-ai/mcp leads on ecosystem, while Supabase is stronger on quality.
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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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