Seah Boon Keong - Chat with OpenDOSM Datasets ranks higher at 47/100 vs Supabase at 42/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Seah Boon Keong - Chat with OpenDOSM Datasets | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 47/100 | 42/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to discover and retrieve datasets from the OpenDOSM collection using a conversational interface. It employs a natural language processing (NLP) engine to interpret user queries and map them to specific datasets, leveraging a curated index of 163 datasets. The integration with the MCP framework enables seamless access to datasets without requiring users to have technical expertise.
Unique: Utilizes a conversational interface that simplifies dataset discovery without requiring technical knowledge, making it accessible to non-technical users.
vs alternatives: More user-friendly than traditional query interfaces, allowing non-technical users to access complex datasets easily.
This capability provides users with tools to perform correlation analysis between different datasets. It uses statistical methods to identify relationships and dependencies among variables, allowing users to input specific datasets and receive correlation metrics. The implementation leverages built-in statistical libraries to compute correlations efficiently and present results in an understandable format.
Unique: Integrates correlation analysis directly into the conversational interface, allowing users to request insights without needing to understand complex statistical methods.
vs alternatives: Faster and more intuitive than standalone statistical software, making it accessible for quick insights.
This capability enables users to perform ARIMA (AutoRegressive Integrated Moving Average) forecasting on selected datasets. It utilizes time series analysis techniques to predict future values based on historical data trends. The tool is designed to automatically handle data preprocessing and model selection, providing users with a straightforward interface to generate forecasts with minimal input.
Unique: Automates the ARIMA modeling process, allowing users to generate forecasts without needing deep statistical knowledge or expertise.
vs alternatives: More accessible than traditional statistical software, enabling quick forecasting for users without a statistical background.
This capability allows users to retrieve the most recent updates from the OpenDOSM datasets. It employs a versioning system to track changes in the datasets and provides users with the latest available data upon request. The implementation ensures that users always have access to the most current information without manual checks.
Unique: Automatically tracks and retrieves the latest updates from a curated dataset collection, ensuring users have access to current information effortlessly.
vs alternatives: More efficient than manual checks for updates, providing instant access to the latest data.
This capability enables users to input natural language queries which are then parsed and transformed into structured queries that can be executed against the dataset. It utilizes NLP techniques to understand user intent and context, ensuring that the queries are accurately interpreted and mapped to the appropriate datasets or functions.
Unique: Employs advanced NLP techniques to convert natural language queries into structured queries seamlessly, enhancing user experience for non-technical users.
vs alternatives: More intuitive than traditional query builders, allowing users to interact with datasets using everyday language.
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.
Seah Boon Keong - Chat with OpenDOSM Datasets scores higher at 47/100 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
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