| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables AI agents and LLM-based applications to control Spotify playback (play, pause, skip, volume adjustment) through the Model Context Protocol, which standardizes tool calling between AI clients and servers. The MCP server acts as a bridge that translates tool invocations from Claude or other MCP-compatible clients into Spotify Web API calls, handling OAuth2 authentication and session management transparently.
Unique: Implements Spotify control as a native MCP tool rather than a custom REST wrapper, enabling seamless integration into Claude's tool-calling ecosystem without requiring developers to write MCP protocol boilerplate themselves
vs alternatives: Simpler than building custom Spotify API integrations because MCP handles the client-server protocol contract; more standardized than direct API calls because it works with any MCP-compatible AI client, not just one platform
Allows AI agents to search Spotify's catalog for tracks, artists, and playlists by translating natural language queries into structured Spotify Search API calls through MCP tool invocations. The server accepts free-form search strings and optional filters (artist, album, type) and returns paginated results with metadata (track duration, popularity, preview URLs, artist info).
Unique: Wraps Spotify's Search API as an MCP tool, enabling AI agents to perform structured searches without developers implementing search UI logic — the agent handles query interpretation and result filtering
vs alternatives: More flexible than hardcoded playlists because it searches Spotify's full catalog dynamically; more natural than REST API calls because the agent can interpret conversational search intent and retry with different query terms
Provides AI agents with real-time visibility into the user's current Spotify playback state (currently playing track, progress, device info, repeat/shuffle modes) and available playback devices through MCP tool calls. The server queries Spotify's Currently Playing and Available Devices endpoints, caching results briefly to reduce API calls while maintaining freshness for agent decision-making.
Unique: Exposes Spotify's playback state as queryable MCP tools rather than requiring agents to maintain their own state model, enabling stateless agent design where each decision is based on fresh API data
vs alternatives: More reliable than client-side state tracking because it always reflects server truth; more efficient than polling because MCP clients can call on-demand rather than continuously syncing
Enables AI agents to access authenticated user's Spotify profile information (display name, follower count, subscription tier) and retrieve their saved/liked tracks library through MCP tool calls. The server implements pagination for the saved tracks endpoint, allowing agents to browse the user's music library and make recommendations based on their existing preferences.
Unique: Exposes user library data as MCP tools, allowing agents to build context about user preferences without requiring custom database storage — the agent can query Spotify as a knowledge source
vs alternatives: More current than cached user preference data because it queries live Spotify library; more privacy-preserving than storing user music history locally because data stays in Spotify's ecosystem
Implements a complete MCP server that handles the Model Context Protocol handshake, tool schema registration, and request/response marshaling for all Spotify capabilities. The server manages OAuth2 authentication flows (authorization code grant), token refresh, and secure credential storage, exposing Spotify operations as standardized MCP tools that Claude and other MCP clients can discover and invoke.
Unique: Provides a complete, working MCP server implementation rather than just API wrapper code, handling protocol details (tool registration, schema validation, error marshaling) that developers would otherwise need to implement themselves
vs alternatives: Simpler than building MCP servers from scratch because it includes OAuth2 flow and token management; more standardized than custom REST wrappers because it follows MCP specification for tool discovery and invocation
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 mcp-spotify at 24/100. mcp-spotify 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
+1 more capabilities