| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Establishes and maintains persistent connections to MongoDB instances and MongoDB Atlas clusters through the Model Context Protocol, handling authentication (connection strings, credentials), connection pooling, and lifecycle management. Implements MCP server transport layer to expose MongoDB as a resource accessible to LLM clients and agents without direct database access.
Unique: Implements MCP server pattern specifically for MongoDB, translating MCP resource and tool calls into MongoDB driver operations, enabling LLMs to interact with databases through a standardized protocol rather than custom integrations
vs alternatives: Provides native MCP integration for MongoDB whereas most alternatives require custom API wrappers or direct driver usage, reducing integration complexity for MCP-compatible clients
Executes MongoDB queries (find, aggregate, insert, update, delete) through MCP tools that accept query parameters and return structured results. Implements query validation and schema introspection to provide type information about collections, enabling LLMs to construct valid queries without trial-and-error. Uses MongoDB's aggregation pipeline and query language natively.
Unique: Combines MCP tool calling with MongoDB's native query language, allowing LLMs to execute complex aggregation pipelines and CRUD operations directly rather than through simplified query builders, preserving MongoDB's full expressiveness
vs alternatives: More powerful than REST API wrappers because it exposes MongoDB's aggregation pipeline and full query syntax through MCP tools, enabling agents to perform complex analytics without intermediate transformation layers
Provides MCP tools to execute geospatial queries on MongoDB collections with 2dsphere or 2d indexes. Implements MongoDB's geospatial operators ($near, $geoWithin, $geoIntersects) enabling agents to find documents based on geographic proximity or containment. Supports GeoJSON format for location data.
Unique: Exposes MongoDB's geospatial query operators through MCP tools, allowing agents to perform location-based searches using GeoJSON, with support for proximity and containment queries without external GIS libraries
vs alternatives: Simpler than integrating external GIS libraries because it uses MongoDB's native geospatial support, enabling agents to perform location-based queries directly on stored GeoJSON data
Provides MCP tools to perform faceted search and analytics using MongoDB's aggregation framework. Agents can request facets (counts by category, range, etc.) alongside search results, and execute complex analytics queries that group, filter, and transform data. Implements multi-facet aggregation pipelines for exploratory data analysis.
Unique: Implements faceted search through MongoDB's aggregation framework, allowing agents to request multiple facets and analytics in a single query, rather than making separate queries for each facet
vs alternatives: More efficient than separate facet queries because it uses MongoDB's aggregation pipeline to compute multiple facets in parallel, reducing round-trips and improving performance
Provides MCP tools to export MongoDB query results in multiple formats (JSON, CSV, BSON) and handle large result sets through pagination or streaming. Implements result formatting and serialization, enabling agents to extract data for external processing or reporting. Supports configurable field selection and transformation during export.
Unique: Implements multi-format data export through MCP tools with built-in pagination support, allowing agents to extract and format MongoDB data for external systems without custom serialization code
vs alternatives: Simpler than custom export scripts because it provides standardized export formats and pagination, enabling agents to extract data consistently across different use cases
Provides MCP tools to list databases, collections, and indexes, and retrieve schema information including field names, types, and validation rules. Implements MongoDB's introspection APIs (listDatabases, listCollections, getIndexes) and potentially uses schema inference or validation metadata to expose structure to LLM clients. Enables agents to discover available data without prior knowledge of the database structure.
Unique: Exposes MongoDB's native introspection APIs through MCP tools, allowing LLMs to dynamically discover database structure at runtime rather than relying on static schema definitions or documentation
vs alternatives: Enables dynamic schema discovery that REST API wrappers typically don't provide, allowing agents to adapt to schema changes without redeployment
Provides a dedicated MCP tool for constructing and executing MongoDB aggregation pipelines, which are multi-stage data transformation workflows. Accepts pipeline stages (match, group, project, sort, limit, etc.) as structured input and executes them server-side, returning transformed results. Implements validation of pipeline syntax and stage compatibility before execution.
Unique: Exposes MongoDB's aggregation pipeline as a first-class MCP tool, allowing LLMs to construct multi-stage data transformations with full access to MongoDB's 30+ aggregation operators, rather than limiting agents to simple queries
vs alternatives: More expressive than simplified query builders because it preserves MongoDB's full aggregation syntax, enabling agents to perform complex analytics that would otherwise require custom code
Provides MCP tools for inserting single documents, inserting multiple documents in bulk, and performing bulk write operations (mixed insert/update/delete). Implements validation of document structure before insertion and handles MongoDB's write concern and error handling. Supports ordered and unordered bulk operations with configurable behavior on partial failures.
Unique: Implements bulk write operations through MCP tools, allowing LLMs to perform efficient batch inserts and mixed write operations without making multiple round-trips, with configurable error handling for partial failures
vs alternatives: Supports bulk operations that simple REST APIs often don't expose, enabling agents to perform efficient batch writes that would otherwise require multiple API calls
+5 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 mongodb-mcp-server at 32/100.
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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