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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables full-text and semantic search across all notes in a Slite workspace through MCP protocol. Implements search queries that traverse the Slite API to index and retrieve notes matching user search terms, returning ranked results with note metadata, content snippets, and hierarchy information for context-aware retrieval.
Unique: Exposes Slite's native search capabilities through MCP protocol, allowing LLM agents and AI applications to query organizational knowledge without custom indexing infrastructure. Integrates directly with Slite's API rather than requiring separate vector database setup.
vs alternatives: Simpler than building custom RAG with external vector databases because it leverages Slite's existing search infrastructure, but less flexible than self-hosted semantic search for custom ranking and filtering.
Provides structured navigation through Slite's note hierarchy (collections, folders, nested notes) via MCP tools. Implements tree-based traversal that maps Slite's organizational structure, allowing clients to browse parent-child relationships, list notes at any level, and retrieve full paths for context-aware navigation without flattening the hierarchy.
Unique: Preserves and exposes Slite's native hierarchical structure through MCP, allowing agents to understand organizational context rather than flattening notes into a list. Implements parent-child relationship tracking that mirrors Slite's actual UI structure.
vs alternatives: More context-aware than flat search because it preserves organizational hierarchy, but requires more API calls than a single flat index for deep traversals.
Fetches complete note content and associated metadata (title, author, creation date, last modified, tags, permissions) from Slite via MCP. Implements direct note access by ID that returns full markdown/rich-text content along with contextual metadata, enabling LLM agents to work with complete note information without multiple round-trips.
Unique: Combines content and metadata retrieval in a single MCP call, reducing round-trips compared to separate API calls. Preserves Slite's native metadata structure (author, timestamps, tags) for context-aware processing by LLM agents.
vs alternatives: More efficient than making separate API calls for content and metadata, but less flexible than custom indexing that could add computed metadata like relevance scores or relationships.
Implements a Model Context Protocol (MCP) server that exposes Slite as a resource and tool provider to MCP-compatible clients (Claude, LLM agents, etc.). Uses MCP's standardized tool and resource schemas to define Slite operations (search, browse, retrieve) as callable functions, enabling seamless integration with any MCP-aware application without custom API wrappers.
Unique: Implements MCP server pattern for Slite, allowing any MCP-compatible client to access Slite without custom integration code. Uses MCP's standardized tool and resource definitions rather than proprietary API wrappers, enabling portability across different AI applications.
vs alternatives: More standardized and portable than custom API wrappers because it uses MCP's open protocol, but requires MCP client support and adds protocol overhead compared to direct API calls.
Extends basic search with optional filtering by metadata (collection, author, date range, tags) and result ranking/sorting capabilities. Implements query construction that builds filtered Slite API requests, allowing users to narrow search scope before retrieval and sort results by relevance, date, or other criteria to surface most useful notes first.
Unique: Adds filtering and ranking on top of Slite's native search, allowing more precise queries without requiring separate post-processing. Implements filter parameter mapping to Slite API's query language, reducing client-side filtering overhead.
vs alternatives: More precise than basic search because it supports filtering and ranking, but less flexible than custom indexing that could enable arbitrary filter combinations and custom relevance algorithms.
Provides workspace-level context (collections, total notes, recent activity, workspace metadata) that AI agents can use to understand the scope and structure of available knowledge. Implements workspace introspection that returns summary statistics and organizational structure, enabling agents to make informed decisions about what to search or browse without blind exploration.
Unique: Provides workspace-level introspection specifically designed for AI agent planning, allowing agents to understand available knowledge scope before making search decisions. Aggregates Slite metadata into a context-aware summary rather than exposing raw API responses.
vs alternatives: More useful for agent planning than raw API responses because it provides structured context about workspace organization, but requires additional API calls compared to on-demand search.
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 slite-mcp at 24/100.
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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
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