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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables users to query the Facebook Ads Library using natural language questions rather than structured filters, translating user intent into API calls against Meta's ad transparency database. The MCP server acts as a semantic intermediary, parsing conversational queries and mapping them to the underlying Ads Library API endpoints, supporting ad discovery across advertiser names, creative content, targeting parameters, and campaign messaging.
Unique: Implements MCP protocol as a bridge to Facebook Ads Library, allowing Claude and other MCP clients to conduct ad research through conversational queries without requiring direct API integration or authentication management by end users
vs alternatives: Provides conversational access to ad transparency data through Claude's native tool-use system, eliminating the need for separate ad research tools or manual API calls while maintaining real-time data from Meta's official Ads Library
Retrieves and structures ad creative assets (images, video thumbnails, copy) from multiple campaigns or advertisers, enabling side-by-side comparison of messaging strategies, visual design patterns, and targeting approaches. The capability aggregates creative metadata and asset URLs from the Ads Library API, formatting results for easy comparative analysis of what messaging resonates with different audience segments.
Unique: Aggregates creative assets and metadata from Facebook Ads Library into structured comparison formats, enabling Claude to synthesize insights across multiple ads without requiring manual asset collection or external design tools
vs alternatives: Provides unified access to official Meta ad creative data through conversational queries, avoiding the need for separate ad intelligence platforms (Adbeat, Semrush) while maintaining real-time accuracy from the source
Retrieves aggregated advertiser metadata from the Facebook Ads Library including ad spend estimates, active campaign counts, targeting strategies, and historical ad activity. The MCP server queries the Ads Library API to build comprehensive advertiser profiles, exposing patterns in spending, creative frequency, and audience targeting that reveal strategic priorities and budget allocation across different market segments.
Unique: Synthesizes advertiser-level insights from the Facebook Ads Library API, aggregating individual ad records into cohesive advertiser profiles with spend estimates and strategic patterns, accessible through natural language queries
vs alternatives: Provides direct access to Meta's official advertiser data through Claude's conversational interface, avoiding reliance on third-party ad intelligence platforms that may have stale or inaccurate data
Enables comparative analysis of how multiple advertisers in the same category approach audience targeting, messaging tone, and creative strategy. The capability retrieves ad records for specified advertisers and structures them for side-by-side comparison, highlighting differences in targeting parameters (age, location, interests), messaging themes, and creative formats used to reach overlapping audience segments.
Unique: Structures multi-advertiser ad data from the Facebook Ads Library into comparative formats that highlight strategic differences in messaging and targeting, enabling Claude to synthesize insights across competitors without manual data collection
vs alternatives: Provides conversational comparative analysis of official Meta ad data, avoiding the need for separate competitive intelligence tools while enabling real-time insights into how competitors are approaching the same audiences
Leverages Claude's reasoning capabilities to synthesize patterns and insights from multiple ad records retrieved from the Facebook Ads Library, generating strategic recommendations based on observed messaging strategies, targeting patterns, and creative approaches. The MCP server retrieves raw ad data, and Claude applies chain-of-thought reasoning to identify trends, gaps, and opportunities in advertiser strategies.
Unique: Combines MCP data retrieval with Claude's extended reasoning to generate strategic insights from ad data, enabling multi-step analysis that connects observed patterns to actionable recommendations without requiring external analytics tools
vs alternatives: Provides conversational strategic analysis of ad data through Claude's native reasoning, eliminating the need for separate business intelligence tools or manual synthesis of competitive ad data
Implements MCP protocol handlers that query the Facebook Ads Library API in real-time, retrieving current ad records and caching results to optimize repeated queries. The server manages API rate limiting, pagination, and error handling, exposing a clean tool interface to Claude for ad data access while abstracting away the complexity of direct API integration and authentication.
Unique: Implements MCP server pattern to expose Facebook Ads Library API as native Claude tools, handling authentication, rate limiting, and pagination server-side while providing a clean, conversational interface for ad data access
vs alternatives: Eliminates the need for users to manage Ads Library API credentials or implement pagination logic, providing seamless integration with Claude's tool-use system through the MCP protocol
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 Facebook Ads Library at 24/100.
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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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