| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes MongoDB Atlas Admin API endpoints to list and retrieve detailed metadata about Atlas projects, including cluster configurations, database names, collection schemas, and project settings. Implements MCP tool bindings that translate natural language requests into authenticated REST calls to Atlas Admin API, parsing JSON responses into structured data for LLM consumption.
Unique: Bridges MongoDB Atlas Admin API directly into MCP protocol, allowing LLMs to query Atlas infrastructure state without custom API wrapper code — uses MCP's standardized tool schema to expose Atlas endpoints as callable functions with automatic authentication handling
vs alternatives: Provides native MCP integration for Atlas management where alternatives require custom REST client code or separate API abstraction layers
Enables programmatic creation of new MongoDB Atlas clusters through MCP tool calls that translate high-level cluster specifications (tier, region, backup settings, network access) into Atlas Admin API provisioning requests. Handles cluster initialization, waits for deployment completion, and returns connection strings and cluster metadata for downstream use.
Unique: Wraps Atlas Admin API cluster creation endpoints in MCP tool schema with built-in parameter validation and sensible defaults, allowing LLMs to provision infrastructure without understanding Atlas API request structure — includes automatic polling for deployment status
vs alternatives: Simpler than Terraform MongoDB provider for ad-hoc cluster creation via LLM because it abstracts state management and provides immediate feedback through MCP protocol
Manages IP whitelist entries and network access rules for Atlas clusters through MCP tools that add, remove, and list IP addresses or CIDR blocks authorized to connect. Implements validation of IP address format and integrates with Atlas Admin API to persist network access policies, enabling dynamic firewall rule management driven by LLM requests.
Unique: Exposes Atlas network access API through MCP tool calls with built-in IP validation and CIDR parsing, allowing LLMs to manage firewall rules without manual API calls — includes list operations for audit trails
vs alternatives: More accessible than raw Atlas API for dynamic access management because MCP tools handle parameter validation and provide human-readable responses
Provisions database users within Atlas clusters through MCP tools that generate credentials, assign roles, and configure authentication methods. Implements secure credential generation, stores credentials in Atlas, and returns connection details for application use. Supports role-based access control (RBAC) with predefined and custom roles.
Unique: Integrates Atlas user provisioning API into MCP tools with automatic credential generation and role validation, allowing LLMs to create database users with appropriate permissions without understanding MongoDB RBAC syntax — returns ready-to-use connection strings
vs alternatives: Simpler than manual user creation in Atlas UI and safer than hardcoding credentials because credentials are generated server-side and returned through secure MCP channels
Manages backup snapshots and restore operations for Atlas clusters through MCP tools that trigger on-demand backups, list available snapshots, and initiate point-in-time restore operations. Implements polling for backup completion and restore status, translating high-level backup intents into Atlas Admin API calls with automatic state tracking.
Unique: Wraps Atlas backup and restore APIs in MCP tools with built-in polling for asynchronous operations, allowing LLMs to trigger backups and restores without managing job status manually — abstracts the complexity of point-in-time restore configuration
vs alternatives: More accessible than raw Atlas API for backup automation because MCP tools handle status polling and provide clear completion signals
Modifies cluster tier, storage allocation, and auto-scaling settings through MCP tools that translate scaling requests into Atlas Admin API calls. Implements validation of tier compatibility, handles scaling operation status tracking, and provides performance metrics context for scaling decisions. Supports both vertical scaling (tier changes) and horizontal scaling (auto-scaling configuration).
Unique: Exposes Atlas cluster scaling API through MCP tools with built-in tier validation and performance metric context, allowing LLMs to make scaling decisions based on cluster health without manual API interaction — includes auto-scaling configuration for hands-off scaling
vs alternatives: More intelligent than simple scaling APIs because it validates tier compatibility and provides performance context for decision-making
Configures monitoring alerts and retrieves cluster performance metrics through MCP tools that interact with Atlas monitoring API. Implements alert rule creation for CPU, memory, connections, and custom metrics, with notification channel integration (email, Slack, PagerDuty). Provides real-time and historical metrics for cluster health assessment.
Unique: Integrates Atlas monitoring and alerting APIs into MCP tools with support for multiple notification channels, allowing LLMs to configure proactive monitoring without manual Atlas UI interaction — provides both alert configuration and real-time metrics retrieval
vs alternatives: More comprehensive than basic metric retrieval because it includes alert rule creation and notification channel integration for end-to-end monitoring automation
Manages Atlas projects and organization settings through MCP tools that create projects, modify project settings, manage team members, and configure organization-level policies. Implements role-based access control for team members, handles project isolation, and provides organization-wide configuration management through Atlas Admin API.
Unique: Exposes Atlas project and organization management APIs through MCP tools with role-based access control, allowing LLMs to manage multi-tenant infrastructure without understanding Atlas permission hierarchy — includes team member provisioning
vs alternatives: Enables programmatic project creation and team management where alternatives require manual Atlas UI interaction or custom Terraform configurations
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 mcp-mongodb-atlas at 24/100. mcp-mongodb-atlas 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
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