| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes ElevenLabs text-to-speech API as an MCP tool, allowing Claude and other MCP clients to invoke voice synthesis without direct API integration. The server translates MCP tool calls into ElevenLabs HTTP requests, handles authentication via API key, and streams or returns audio file references. Implements the Model Context Protocol's tool-calling interface to bridge LLM agents with ElevenLabs' neural TTS engine.
Unique: Implements ElevenLabs TTS as a native MCP tool, enabling seamless integration into Claude and other MCP clients without custom API wrappers — uses MCP's standardized tool schema to expose voice synthesis as a first-class capability within the protocol
vs alternatives: Simpler than building custom API clients for each LLM platform; more flexible than ElevenLabs' native integrations because it works with any MCP-compatible client, not just specific platforms
Provides MCP tools to list available ElevenLabs voices, retrieve voice metadata (name, language, accent, preview audio), and select voices for synthesis. The server queries ElevenLabs' voice API endpoint and exposes voice information as structured data, allowing agents to programmatically choose voices based on language, gender, or other attributes without hardcoding voice IDs.
Unique: Exposes ElevenLabs voice catalog as queryable MCP tools, enabling agents to discover and reason about available voices programmatically rather than relying on hardcoded voice IDs or external documentation
vs alternatives: More discoverable than static voice ID lists; integrates voice selection directly into agent workflows without requiring separate API calls or manual configuration
Implements the Model Context Protocol (MCP) server specification, exposing ElevenLabs capabilities as standardized MCP tools and resources. The server handles MCP initialization, tool registration, request routing, and error handling according to the MCP specification. It acts as a bridge between MCP clients (like Claude) and the ElevenLabs API, translating MCP calls into ElevenLabs HTTP requests and returning results in MCP-compliant format.
Unique: Provides a complete MCP server implementation for ElevenLabs, handling all protocol-level concerns (initialization, tool registration, request routing) so developers don't need to implement MCP boilerplate themselves
vs alternatives: More maintainable than custom API wrappers because it adheres to a standard protocol; more flexible than ElevenLabs' native integrations because it works with any MCP client
Exposes ElevenLabs TTS synthesis parameters (stability, similarity_boost, style, use_speaker_boost) as configurable MCP tool inputs, allowing agents to fine-tune voice characteristics and synthesis behavior. The server passes these parameters directly to the ElevenLabs API, enabling control over voice consistency, emotional tone, and speaker emphasis without requiring multiple API calls or voice cloning.
Unique: Exposes ElevenLabs' full parameter set as MCP tool inputs, enabling agents to programmatically control voice characteristics without requiring separate API calls or configuration files
vs alternatives: More flexible than fixed voice presets; allows agents to adapt synthesis behavior dynamically based on content or user preferences
Translates ElevenLabs API errors and responses into MCP-compliant error messages and structured results. The server catches HTTP errors from ElevenLabs (authentication failures, rate limits, invalid parameters), maps them to appropriate MCP error codes, and returns human-readable error messages to the client. This abstraction shields MCP clients from ElevenLabs API details and enables consistent error handling across the MCP ecosystem.
Unique: Provides a translation layer between ElevenLabs API errors and MCP protocol errors, ensuring consistent error handling and enabling agents to reason about failure modes without deep knowledge of ElevenLabs internals
vs alternatives: More robust than direct API error propagation; enables better error recovery and debugging compared to opaque API failures
Manages ElevenLabs API authentication by reading API keys from environment variables, configuration files, or secure credential stores. Implements secure credential handling (no logging of secrets, proper scoping) and validates API key validity before making requests. Provides clear error messages when credentials are missing or invalid, guiding users to set up authentication correctly.
Unique: Implements secure API key management for ElevenLabs, reading credentials from environment or config without exposing them in logs or error messages. Validates credentials at startup and provides clear guidance for setup, reducing common configuration errors.
vs alternatives: Centralized credential management in MCP server eliminates need for clients to handle API keys directly; environment-based configuration follows security best practices, whereas hardcoding keys in client code is a security risk.
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 elevenlabs-mcp at 24/100. elevenlabs-mcp 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
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