| Ecosystem |
| 0 |
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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a FastMCP-based middleware layer that translates MCP tool requests into authenticated Facebook Graph API calls using the requests HTTP client. The server.py entry point handles JSON-RPC protocol communication over stdin/stdout, avoiding network port dependencies and enabling direct integration with MCP clients like Claude Desktop and Cursor IDE. Each of the 21 MCP tools maps to specific Graph API endpoints with automatic request/response serialization.
Unique: Uses FastMCP framework for native MCP protocol implementation with stdio-based communication, eliminating network port management and enabling seamless integration with Claude Desktop and Cursor IDE without custom protocol handling code
vs alternatives: Simpler deployment than REST API wrappers because it avoids port configuration and network exposure, and more standardized than direct Graph API calls because it implements the MCP specification for cross-client compatibility
Provides 7 account-level MCP tools that aggregate data across the full Facebook Ads entity hierarchy (accounts → campaigns → ad sets → ads → insights). Tools query the Graph API with account ID as the root parameter and traverse child entities, returning paginated results with filtering and field selection. Implements the Facebook Ads object model where accounts contain campaigns, campaigns contain ad sets, and ad sets contain individual ads with associated creative and performance data.
Unique: Implements account-level aggregation across Facebook's full entity hierarchy (accounts → campaigns → ad sets → ads) with automatic pagination handling and field selection, exposing the complete advertising structure through a single account ID entry point
vs alternatives: More comprehensive than single-entity tools because it provides account-wide visibility in one operation, and more efficient than making separate API calls for each entity type because pagination and hierarchy traversal are handled server-side
Exposes MCP tools for creating and updating Facebook Ads campaigns and ad sets with full parameter control over budget allocation, scheduling, targeting criteria, and optimization objectives. Tools construct Graph API POST requests with campaign/ad set objects, validating required fields (name, objective, budget_type) and optional targeting parameters (age, location, interests, custom audiences). Supports both daily and lifetime budgets, campaign scheduling with start/end dates, and objective selection (REACH, TRAFFIC, CONVERSIONS, etc.).
Unique: Provides full campaign and ad set creation with integrated budget allocation, scheduling, and targeting configuration in a single MCP tool call, abstracting away Graph API endpoint complexity and parameter validation
vs alternatives: More complete than basic campaign creation because it includes targeting and budget configuration in one operation, and more flexible than Facebook Ads Manager templates because it accepts programmatic parameters for dynamic campaign generation
Exposes MCP tools for creating and managing ad creatives (images, videos, carousels) and ad variants within ad sets. Tools handle creative asset specification (image URLs, video URLs, or carousel card definitions), copy text, headlines, and call-to-action buttons. Supports creating multiple ad variants from a single ad set to enable A/B testing. Implements the Facebook Ads creative object model where creatives are associated with ads, and ads are associated with ad sets, enabling multi-variant campaign testing.
Unique: Integrates creative asset specification (images, videos, carousels) with ad variant creation in a single MCP tool, enabling programmatic A/B testing without separate asset management steps
vs alternatives: More streamlined than manual Facebook Ads Manager because it creates multiple ad variants in one operation, and more flexible than template-based systems because it accepts dynamic creative parameters for each variant
Provides MCP tools for querying Facebook Ads performance metrics (spend, impressions, clicks, conversions, ROAS, CPC, CTR) at account, campaign, ad set, and ad levels. Tools construct Graph API requests with date range parameters and metric field selectors, returning time-series or aggregated data. Implements Facebook's insights API with automatic metric calculation (e.g., CTR = clicks / impressions) and supports breakdowns by device, platform, and demographic. Data has 1-day latency from Facebook's reporting pipeline.
Unique: Aggregates Facebook Ads insights across entity hierarchy levels (account → campaign → ad set → ad) with automatic metric calculation and optional demographic/device breakdowns, abstracting away Graph API pagination and metric field complexity
vs alternatives: More comprehensive than manual Facebook Ads Manager exports because it supports programmatic date ranges and metric selection, and more flexible than static reports because it enables dynamic queries for custom analysis windows
Exposes MCP tools for updating campaign and ad set status (ACTIVE, PAUSED, DELETED) and budget parameters (daily_budget, lifetime_budget, budget_remaining) in real-time. Tools construct Graph API PATCH requests with status and budget fields, enabling immediate campaign pause/resume and budget adjustment without Facebook Ads Manager UI. Changes propagate to Facebook's system within seconds, affecting ad delivery immediately.
Unique: Enables real-time campaign status and budget updates through MCP tools with immediate Facebook Ads system propagation, allowing AI agents to implement reactive optimization rules without polling or manual intervention
vs alternatives: Faster than Facebook Ads Manager UI because changes execute in seconds via API, and more flexible than scheduled rules because it enables dynamic decision-making based on real-time performance data
Supports three distinct deployment paths (automated GoMarble setup, manual development configuration, Claude Desktop CLI installation) that converge to the same operational state with 21 MCP tools available. Authentication uses Meta access tokens passed via environment variables (FACEBOOK_ACCESS_TOKEN) or configuration files, with optional integration to GoMarble's token service for automated token refresh. The server.py entry point accepts command-line arguments for token and account ID, enabling flexible deployment across local development, Docker containers, and cloud environments.
Unique: Provides three distinct deployment paths (automated, manual, CLI-based) that all converge to identical MCP tool availability, enabling flexible deployment across development, containerized, and desktop environments without code changes
vs alternatives: More flexible than single-deployment-method tools because it supports local development, Docker, and Claude Desktop without requiring different codebases, and simpler than manual API integration because authentication is environment-driven
Provides MCP tools for specifying and applying audience targeting parameters (age ranges, locations, interests, custom audiences, lookalike audiences) when creating ad sets. Tools accept targeting objects with demographic filters, geographic location codes, Facebook interest category IDs, and references to pre-existing custom audiences. Implements Facebook's targeting taxonomy with validation of location codes (country, region, city) and interest category IDs. Does not create audiences; only applies existing audience definitions to ad sets.
Unique: Integrates demographic, geographic, interest, and custom audience targeting into a single ad set creation tool with validation against Facebook's targeting taxonomy, enabling complex audience specification without separate targeting API calls
vs alternatives: More comprehensive than basic demographic targeting because it combines interests, locations, and custom audiences in one operation, and more flexible than preset audience templates because it accepts programmatic targeting parameters
+1 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 Facebook Ads at 25/100.
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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