| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables AI agents to programmatically create and launch Facebook/Instagram ad campaigns by translating natural language campaign specifications into Meta Ads API calls. The MCP server abstracts Meta's complex campaign hierarchy (account → campaign → ad set → ad) into a simplified interface, handling authentication via stored API credentials and managing the full campaign lifecycle from creation through initial deployment.
Unique: Implements MCP protocol binding to Meta Ads API with agent-friendly abstraction layer that handles the 4-tier campaign hierarchy and credential management, allowing natural language campaign specifications to be translated into valid API payloads without requiring agents to understand Meta's schema complexity
vs alternatives: Provides direct MCP integration for agentic control over Meta campaigns (vs. Meta's native dashboard or third-party tools like Hootsuite that require manual approval workflows), enabling fully autonomous campaign deployment within agent reasoning loops
Mirrors Meta capability for TikTok Ads Manager, allowing agents to create and launch TikTok ad campaigns by translating specifications into TikTok Ads API calls. Handles TikTok's advertiser account structure, audience targeting via TikTok's pixel and interest taxonomy, and creative asset validation specific to TikTok's format requirements (vertical video, sound requirements, etc.).
Unique: Provides MCP binding to TikTok Ads API with built-in validation for TikTok-specific creative requirements (vertical video format, sound compatibility, duration limits) and audience targeting via TikTok's interest taxonomy, enabling agents to deploy TikTok campaigns without manual format conversion or validation
vs alternatives: Enables agentic control over TikTok campaigns (vs. TikTok's dashboard-only workflow or third-party tools requiring manual approval), with native support for TikTok's stricter creative requirements and audience targeting model
Implements robust error handling for campaign operations, capturing API errors, validation failures, and platform-specific issues, then providing agents with structured error information and retry guidance. Distinguishes between retryable errors (rate limits, temporary API issues) and non-retryable errors (invalid parameters, authentication failures).
Unique: Implements MCP-level error handling that classifies errors as retryable or non-retryable and provides agents with structured error information and corrective guidance, enabling robust autonomous campaign operations
vs alternatives: Provides agents with actionable error information and retry guidance (vs. raw API errors), enabling graceful error recovery and reducing failed operations
Implements the Model Context Protocol (MCP) server interface, defining tools and resources that expose campaign management capabilities to AI agents. Uses JSON schema to define tool inputs/outputs and implements the MCP transport layer for communication with MCP clients (Claude Desktop, custom agent frameworks).
Unique: Implements full MCP server interface with JSON schema definitions for all campaign management tools, enabling standardized integration with MCP-compatible AI agents and providing schema-based tool discovery
vs alternatives: Provides standardized MCP integration (vs. custom API integrations), enabling agents to discover and use campaign management tools through a standard protocol
Coordinates simultaneous campaign creation and deployment across Meta and TikTok platforms through a unified MCP interface, allowing agents to manage campaign lifecycle decisions (budget allocation, audience overlap, creative adaptation) across both platforms in a single agentic reasoning step. Handles platform-specific parameter translation and error handling to ensure consistency across disparate APIs.
Unique: Implements MCP-level orchestration that translates a single unified campaign specification into platform-specific API calls, handling parameter mapping, creative adaptation, and error recovery across Meta and TikTok without requiring agents to understand each platform's API schema
vs alternatives: Provides true multi-platform campaign orchestration within agent reasoning (vs. sequential manual steps or third-party tools requiring separate workflows), enabling agents to make cross-platform budget and creative decisions in a single reasoning loop
Validates campaign specifications against Meta and TikTok API schemas before deployment, catching invalid parameters (budget ranges, audience targeting options, creative format requirements) and providing structured error messages that agents can use to correct specifications. Uses schema definitions for each platform to enforce constraints like minimum/maximum budgets, valid audience targeting categories, and required creative fields.
Unique: Implements pre-flight validation layer that enforces both Meta and TikTok API schemas within the MCP server, providing agents with structured validation errors and correction suggestions before API calls are attempted, reducing failed deployments and API quota waste
vs alternatives: Catches validation errors before API calls (vs. discovering errors after deployment and wasting API quota), providing agents with actionable correction guidance rather than raw API error messages
Retrieves current campaign status, performance metadata, and configuration details from Meta and TikTok APIs, allowing agents to query campaign state without manual dashboard access. Returns structured campaign metadata including creation timestamp, budget, audience targeting, creative assets, and current status (active, paused, pending review, rejected).
Unique: Provides MCP-based campaign metadata retrieval that abstracts Meta and TikTok's different response schemas into a unified structure, allowing agents to query campaign state across both platforms with consistent response formats
vs alternatives: Enables agents to verify campaign deployment and retrieve configuration details programmatically (vs. manual dashboard checks), with unified response format across Meta and TikTok reducing agent complexity
Allows agents to pause and resume active campaigns on Meta and TikTok by translating pause/resume commands into API calls that update campaign status. Handles the state machine for campaign status transitions (active → paused, paused → active) and validates that campaigns are in valid states before attempting status changes.
Unique: Implements MCP-based campaign control that validates state transitions before executing pause/resume commands, preventing invalid operations and providing agents with clear feedback on campaign status changes
vs alternatives: Enables agents to control campaign spend dynamically without manual dashboard access (vs. static campaigns or third-party tools requiring approval workflows), with built-in state validation preventing invalid transitions
+4 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 Nolas-Shadow 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