| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 16 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables AI agents to send messages to Zulip streams and topics, and retrieve message history with full context. Implements MCP tool bindings that translate agent requests into Zulip REST API calls, handling authentication via API tokens and managing message formatting for rich text rendering. Supports querying messages by stream, topic, sender, and date range with pagination for large result sets.
Unique: Exposes Zulip's REST API as MCP tools with automatic token-based authentication and pagination handling, allowing Claude and other MCP clients to interact with Zulip without writing custom API code. Implements 60+ discrete tools covering messaging, streams, and events as separate callable functions rather than a single generic API wrapper.
vs alternatives: Provides tighter integration than generic REST API clients because it abstracts Zulip-specific authentication, pagination, and error handling into pre-built MCP tools that Claude can invoke directly without boilerplate.
Allows AI agents to create, list, and manage Zulip streams and topics programmatically through MCP tool bindings. Implements CRUD operations that map to Zulip's stream management API, handling permissions validation, stream visibility settings (public/private), and topic creation within existing streams. Supports querying stream metadata including subscriber counts, creation dates, and description fields.
Unique: Exposes stream and topic CRUD operations as discrete MCP tools rather than requiring agents to construct raw API calls, with built-in handling of Zulip's permission model and stream visibility constraints. Enables agents to reason about stream organization and make autonomous decisions about workspace structure.
vs alternatives: More accessible than raw Zulip API for AI agents because it abstracts permission checks and stream metadata queries into single-purpose tools that don't require understanding Zulip's permission hierarchy.
Allows AI agents to pin important messages to streams or topics, and manage bookmarks for individual users through MCP tools that map to Zulip's pinning and bookmarking APIs. Implements pin/bookmark enumeration with metadata (pinner, timestamp, reason), supporting agents in highlighting important information or decisions. Handles pin removal and bookmark management for conversation curation.
Unique: Exposes Zulip's pinning and bookmarking APIs as MCP tools, allowing agents to curate important information and highlight key decisions. Provides pin metadata for analyzing conversation importance and decision history.
vs alternatives: More lightweight than external documentation systems because agents can pin messages directly in Zulip, keeping important information in context without requiring separate tools or manual documentation.
Enables AI agents to compose draft messages, schedule messages for future delivery, and manage message templates through MCP tools that integrate with Zulip's drafts and scheduling APIs. Implements draft persistence, template variable substitution, and scheduled message queuing with delivery time management. Supports agents in preparing messages without immediate posting, enabling review or conditional delivery.
Unique: Implements MCP tools for draft composition and message scheduling, allowing agents to prepare messages without immediate posting and schedule delivery at specific times. Supports template-based message generation for consistent formatting.
vs alternatives: More flexible than immediate message posting because agents can draft messages for review, schedule delivery for optimal timing, and reuse templates for consistency without manual message composition.
Provides MCP tools to edit and delete messages, with support for tracking edit history. Implements message modification by wrapping Zulip's /messages/{message_id} endpoint, allowing agents to update message content and delete messages. Supports retrieving edit history to maintain an audit trail of changes.
Unique: Provides message editing and deletion through MCP with edit history tracking, enabling agents to modify messages and maintain audit trails. Supports both content and topic edits.
vs alternatives: Enables message lifecycle management that most Zulip bots lack, supporting message updates and corrections after sending
Provides MCP tools to schedule messages for future delivery and manage message queues. Implements scheduling by storing message metadata and delivery times, allowing agents to compose messages that are sent at specified times. Supports queue management with priority levels and batch sending capabilities.
Unique: Provides message scheduling and queuing capabilities through MCP, enabling agents to send messages at future times. Requires external state management but supports complex scheduling workflows.
vs alternatives: Enables time-based automation that most Zulip bots lack, requiring agents to implement scheduling logic externally
Exposes MCP tools to pin and unpin messages, marking them as important or featured. Implements pinning by wrapping Zulip's message pinning API, allowing agents to highlight important messages in streams or topics. Supports querying pinned messages to retrieve important content.
Unique: Provides message pinning and unpinning through MCP, enabling agents to highlight important content. Supports querying pinned messages for content discovery.
vs alternatives: Enables content curation workflows that most Zulip bots ignore, treating pinned messages as a first-class feature
Provides MCP tools to rename topics and reorganize messages across streams. Implements topic management by wrapping Zulip's topic editing API, allowing agents to rename topics for clarity and move messages between topics or streams. Supports bulk topic operations for workspace reorganization.
Unique: Provides topic renaming and message reorganization through MCP, enabling agents to manage workspace structure dynamically. Supports bulk operations for large-scale reorganization.
vs alternatives: Enables automated workspace organization that most Zulip bots lack, requiring manual topic management
+8 more capabilities
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 Zulip at 29/100.
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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