| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes CRM operations (contacts, deals, activities) through the Model Context Protocol, allowing Claude and other MCP-compatible clients to read and write CRM data by translating natural language requests into structured API calls. Implements MCP's resource and tool abstractions to map CRM entities to discoverable, type-safe endpoints that clients can introspect and invoke.
Unique: Implements MCP as a first-class integration pattern for CRM access, allowing Claude to discover and invoke CRM operations through standardized MCP resource and tool schemas rather than custom API wrappers or plugins
vs alternatives: Provides tighter Claude integration than REST API wrappers or Zapier automations because MCP allows Claude to understand CRM schema natively and compose multi-step CRM workflows in a single conversation
Enables Claude to create, retrieve, update, and search contacts/leads in the CRM by translating conversational requests into structured contact operations. Implements MCP tools that map contact fields (name, email, phone, company, tags) to callable functions, with built-in validation and error handling for malformed or incomplete contact data.
Unique: Exposes contact operations as MCP tools with schema-based validation, allowing Claude to understand contact field requirements and constraints before attempting operations, reducing failed API calls compared to untyped function calling
vs alternatives: More reliable than generic CRM API wrappers because MCP schema enforcement prevents Claude from submitting invalid contact data, and natural language parsing is optimized for sales workflows (e.g., parsing company names from email domains)
Allows Claude to view, create, and advance deals through CRM pipeline stages by exposing deal operations as MCP tools. Implements stage transitions with validation (e.g., preventing moves to invalid stages) and supports deal metadata (amount, close date, probability) to enable Claude to reason about pipeline health and forecast revenue.
Unique: Integrates deal operations with MCP's tool schema to enable Claude to reason about pipeline state and make stage transitions based on conversation context, rather than requiring manual CRM updates
vs alternatives: Enables more intelligent pipeline management than Zapier automations because Claude can analyze deal metadata and customer communication in a single context before deciding on stage transitions
Enables Claude to log activities (calls, emails, meetings, notes) against contacts and deals, creating an audit trail and timeline of customer interactions. Implements MCP tools that map activity types to structured logging functions, with automatic timestamps and optional association with deals or contacts.
Unique: Provides MCP-based activity logging that Claude can invoke contextually during conversations, creating a persistent record of AI-assisted interactions without requiring manual CRM data entry
vs alternatives: More seamless than manual activity logging because Claude can automatically create activity records from conversation summaries, reducing friction compared to sales reps manually typing notes into the CRM
Exposes CRM data structures (contacts, deals, activities, custom fields) as MCP resources with JSON schema definitions, allowing MCP clients to discover available operations and understand field requirements without external documentation. Implements MCP's resource listing and schema endpoints to provide runtime introspection of CRM capabilities.
Unique: Implements MCP resource discovery as a first-class feature, allowing clients to understand CRM capabilities dynamically rather than relying on hardcoded tool definitions or external documentation
vs alternatives: More flexible than static API documentation because MCP clients can adapt to different CRM configurations at runtime, enabling portable agents that work across multiple CRM instances
Provides structured error responses and pre-operation validation for CRM operations, catching common mistakes (missing required fields, invalid stage transitions, non-existent contacts) before they reach the CRM API. Implements validation logic at the MCP tool layer with detailed error messages that Claude can use to correct operations.
Unique: Implements validation at the MCP tool layer, allowing Claude to understand and correct errors in natural language rather than receiving opaque API errors from the CRM
vs alternatives: More user-friendly than raw CRM API errors because validation messages are tailored for Claude's understanding, enabling self-correction without human intervention
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 mcpgrowcrm1 at 24/100. mcpgrowcrm1 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