| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Audiense demographic analysis as MCP tools, allowing Claude and other LLM agents to query audience segments by age, gender, location, and income without direct API calls. Implements MCP resource and tool abstractions that translate natural language queries into structured Audiense API requests, returning parsed demographic distributions and segment profiles.
Unique: Wraps Audiense's proprietary demographic API as MCP tools, enabling LLM agents to perform audience analysis without direct API integration code. Uses MCP's standardized tool schema to abstract Audiense's REST endpoints, allowing Claude and other agents to compose demographic queries into multi-step workflows.
vs alternatives: Simpler than building custom Audiense API integrations because MCP handles credential management and tool discovery; more flexible than Audiense's native UI because agents can combine demographic data with other MCP tools in a single workflow.
Retrieves cultural and psychographic attributes of audiences (values, interests, lifestyle segments, cultural affinities) from Audiense Insights and exposes them as queryable MCP resources. Translates LLM requests into Audiense psychographic API calls, returning structured profiles that describe audience mindsets, cultural preferences, and behavioral patterns beyond demographics.
Unique: Exposes Audiense's proprietary psychographic modeling (cultural values, lifestyle segments, behavioral affinities) through MCP, enabling LLMs to reason about audience mindsets and cultural alignment without requiring marketing domain expertise from the developer.
vs alternatives: Richer than demographic-only tools because it captures values and lifestyle data; more accessible than raw Audiense API because MCP abstracts authentication and schema negotiation, allowing non-technical users to query psychographics via natural language.
Queries Audiense's influencer database to identify and rank influential accounts within a target audience, returning influencer profiles with reach, engagement metrics, and audience overlap. Implements MCP tools that translate LLM requests into Audiense influencer API calls, filtering by niche, follower count, engagement rate, and audience alignment to surface relevant micro and macro influencers.
Unique: Integrates Audiense's influencer database as MCP tools, enabling LLM agents to perform multi-criteria influencer discovery (reach, engagement, audience alignment) without building custom ranking logic. Uses MCP's tool schema to expose filtering and sorting capabilities as composable operations.
vs alternatives: More integrated than manual Audiense UI searches because agents can chain influencer discovery with audience analysis and content strategy in a single workflow; more targeted than generic influencer platforms because it filters by audience alignment, not just follower count.
Analyzes content performance and engagement patterns within a target audience, returning insights on which content types, topics, and formats drive engagement. Implements MCP tools that query Audiense's content engagement data, identifying trending topics, optimal posting times, and content preferences specific to an audience segment.
Unique: Exposes Audiense's content engagement analytics as MCP tools, enabling LLMs to analyze what content resonates with specific audiences without requiring manual data export or dashboard navigation. Abstracts Audiense's engagement API to provide topic, format, and timing insights in a single query.
vs alternatives: More actionable than generic social analytics because it's audience-specific; more accessible than Audiense's native dashboard because LLM agents can query and synthesize insights programmatically, enabling automated content strategy generation.
Orchestrates multiple Audiense MCP tools (demographics, psychographics, influencers, content engagement) within a single LLM agent workflow, enabling complex audience analysis that combines insights from multiple data sources. Implements MCP's tool composition pattern, allowing Claude and other agents to chain demographic queries with psychographic analysis and influencer discovery in a single multi-step reasoning process.
Unique: Enables LLM agents to compose multiple Audiense MCP tools into coherent multi-step workflows, treating audience intelligence as a reasoning problem rather than isolated data queries. Uses MCP's tool discovery and composition patterns to allow agents to dynamically select and chain tools based on analysis goals.
vs alternatives: More powerful than individual tools because agents can synthesize insights across demographics, psychographics, and influencers in a single workflow; more flexible than pre-built Audiense reports because LLMs can adapt analysis based on specific business questions and iterate on insights.
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 Audiense Insights at 23/100.
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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