| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables LLM clients to query Slite workspaces through the Model Context Protocol, translating MCP tool calls into Slite API requests and returning structured search results. Implements MCP server specification with stdio transport, allowing Claude and other MCP-compatible clients to discover and invoke Slite search as a native tool without custom integration code.
Unique: Implements MCP server specification as a bridge to Slite, allowing any MCP-compatible LLM client (Claude, custom agents) to treat Slite as a native tool without custom SDK integration — uses stdio transport for seamless subprocess communication with LLM hosts
vs alternatives: Eliminates custom API wrapper code by leveraging MCP's standardized tool discovery and invocation protocol, making Slite accessible to any MCP client vs. building client-specific integrations
Automatically generates and exposes MCP-compliant tool schemas that describe Slite search capabilities to LLM clients, including parameter definitions, descriptions, and required fields. The server introspects Slite API capabilities and translates them into OpenAI-compatible JSON Schema format that MCP clients use for tool discovery and validation.
Unique: Generates MCP tool schemas by introspecting Slite API at server startup, translating Slite's native API documentation into standardized JSON Schema format that MCP clients can parse and validate — enables zero-configuration tool discovery
vs alternatives: Provides automatic schema generation vs. manual tool definition, reducing maintenance burden when Slite API changes and enabling clients to discover capabilities dynamically
Handles secure storage and injection of Slite API credentials (API key, workspace ID) into outbound requests to Slite API endpoints. Implements credential loading from environment variables or configuration files, with support for multiple authentication schemes that Slite API may require (bearer tokens, API key headers).
Unique: Implements credential loading from standard Node.js environment patterns (dotenv, process.env) with support for Slite's specific authentication headers, avoiding custom credential storage logic and integrating with existing DevOps practices
vs alternatives: Uses standard environment variable patterns vs. custom credential stores, making it compatible with existing CI/CD and secret management tools (AWS Secrets Manager, HashiCorp Vault, etc.)
Implements the MCP server-side stdio transport layer, enabling the Node.js process to communicate with MCP clients (Claude Desktop, custom agents) via standard input/output streams. Handles JSON-RPC message framing, request/response routing, and error serialization according to MCP protocol specification.
Unique: Implements MCP stdio transport as a Node.js subprocess server, using JSON-RPC framing over stdin/stdout to communicate with parent MCP clients — enables zero-configuration integration with Claude Desktop and other MCP hosts
vs alternatives: Stdio transport is simpler and more portable than HTTP/WebSocket alternatives, requiring no port management or network configuration, making it ideal for local development and Claude Desktop integration
Translates MCP tool invocation requests into Slite API calls, maps Slite API responses back to MCP-compatible formats, and handles errors with proper MCP error codes and messages. Implements request validation, response normalization, and graceful degradation for API failures or rate limiting.
Unique: Implements bidirectional translation between MCP tool protocol and Slite API, with explicit error mapping to MCP error codes — ensures that Slite API failures are communicated to LLM clients in a standardized way
vs alternatives: Provides explicit error handling and response normalization vs. raw API passthrough, making Slite failures transparent to LLM clients and enabling better error recovery in agent workflows
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 45/100 vs slite-mcp-server at 26/100. slite-mcp-server 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