| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes HubSpot contact CRUD operations through MCP tool schema, enabling AI agents to create, read, update, and delete contacts with property mapping to HubSpot's contact object model. Implements bidirectional sync between agent state and HubSpot's contact database through standardized MCP tool definitions that translate agent requests into HubSpot API calls with automatic property validation and error handling.
Unique: Official HubSpot implementation ensures 100% API compatibility and immediate support for new HubSpot features; uses MCP tool schema to expose contact operations as first-class agent capabilities rather than requiring custom API wrappers
vs alternatives: More reliable than third-party HubSpot integrations because it's maintained by HubSpot engineers and automatically tracks API changes without lag
Provides MCP tools to navigate HubSpot's company object graph, including parent-child relationships, associated contacts, and deal pipelines. Implements hierarchical queries that allow agents to understand organizational structure and relationships without requiring multiple sequential API calls, using HubSpot's association API under the hood to resolve company-to-contact and company-to-deal relationships.
Unique: Exposes HubSpot's association API through MCP tools, allowing agents to reason about organizational relationships as first-class entities rather than requiring custom graph traversal logic
vs alternatives: Directly integrates with HubSpot's native association model, avoiding data inconsistencies that arise from custom relationship tracking in external systems
Exposes deal CRUD operations and pipeline stage transitions through MCP tools, enabling agents to move deals through sales pipelines, update deal properties (amount, close date, probability), and retrieve pipeline analytics. Implements validation of stage transitions based on HubSpot's pipeline configuration, preventing invalid state changes and maintaining data integrity across the sales process.
Unique: Validates stage transitions against HubSpot's pipeline schema, preventing agents from creating invalid deal states; integrates with HubSpot's deal property system for rich metadata
vs alternatives: Native HubSpot integration ensures deal stage transitions respect all custom pipeline rules and dependencies, unlike generic CRM APIs that treat pipelines as simple state machines
Provides MCP tools for creating, updating, and retrieving support tickets with status tracking, priority assignment, and assignment to support agents. Implements ticket lifecycle management including status transitions (new → open → waiting on customer → resolved → closed) and integrates with HubSpot's service hub to track customer support interactions and SLA compliance.
Unique: Integrates with HubSpot Service Hub's ticket lifecycle model, including SLA tracking and status validation, rather than treating tickets as generic objects
vs alternatives: Direct HubSpot integration provides automatic SLA calculation and status validation, whereas generic ticketing APIs require custom SLA logic implementation
Exposes HubSpot's marketing email and workflow automation capabilities through MCP tools, enabling agents to trigger email sequences, enroll contacts in workflows, and retrieve campaign performance metrics. Implements workflow state management and email personalization by integrating with HubSpot's contact property system to dynamically populate email variables and conditional logic.
Unique: Integrates with HubSpot's workflow engine for stateful automation, allowing agents to trigger complex multi-step sequences rather than just sending individual emails
vs alternatives: Workflow-based approach enables agents to execute sophisticated marketing automation with branching logic and delays, unlike simple email APIs that only support individual message sending
Provides MCP tools to query HubSpot's contact property schema, including property names, types, validation rules, and custom properties. Enables agents to dynamically discover available properties and validate property values before attempting updates, preventing API errors from invalid property assignments and enabling self-correcting agent behavior.
Unique: Exposes HubSpot's property schema as queryable MCP tools, enabling agents to dynamically adapt to custom property configurations rather than relying on hardcoded property mappings
vs alternatives: Schema introspection capability allows agents to handle custom HubSpot configurations without code changes, whereas static API integrations require manual configuration updates
Provides MCP tools for batch operations on contacts and companies (create, update, delete multiple records in single requests), with granular error handling that reports per-record success/failure. Implements request batching to respect HubSpot API rate limits while maximizing throughput, and provides detailed error messages for failed records to enable agent retry logic.
Unique: Implements per-record error reporting in batch operations, allowing agents to identify and retry failed records rather than failing entire batches
vs alternatives: Granular error handling enables agents to handle partial failures intelligently, whereas simple batch APIs treat entire batches as atomic all-or-nothing operations
Provides MCP tools to retrieve contact activity history including emails sent/received, meetings, calls, form submissions, and website visits. Implements timeline aggregation across multiple HubSpot modules (email, meetings, forms, tracking) to provide agents with a unified view of customer engagement without requiring separate API calls per activity type.
Unique: Aggregates activity across HubSpot's multiple modules (email, meetings, forms, tracking) into a unified timeline, providing agents with comprehensive engagement context
vs alternatives: Unified activity timeline eliminates need for agents to query multiple APIs separately, reducing latency and enabling more sophisticated engagement-based reasoning
+2 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.
HubSpot MCP Server scores higher at 60/100 vs Supabase at 42/100.
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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