MCP Server for Singapore Government Open Data ranks higher at 49/100 vs Supabase at 42/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | MCP Server for Singapore Government Open Data | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 49/100 | 42/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables natural language queries against the data.gov.sg catalog by translating user search terms into API calls that match datasets by title, description, and metadata tags. Implements a search abstraction layer that normalizes query parameters and returns ranked results with relevance scoring, allowing developers to discover relevant datasets without manual catalog browsing.
Unique: Wraps data.gov.sg's REST API as MCP tools, enabling LLM-native dataset discovery without requiring developers to write API integration code; specifically optimized for Singapore government data structures and agency hierarchies
vs alternatives: Provides direct MCP integration to Singapore government data (vs generic data APIs), reducing context switching for agents analyzing local government datasets
Fetches complete metadata for a specific dataset including schema information, column definitions, data types, and update frequency. Implements a metadata normalization layer that parses data.gov.sg's API responses and exposes structured schema details, enabling developers to understand dataset structure before download without inspecting raw files.
Unique: Normalizes heterogeneous metadata from data.gov.sg (which uses multiple schema formats across agencies) into a consistent structured format, with explicit handling of Singapore-specific data classifications and update cadences
vs alternatives: Provides schema-aware metadata retrieval specifically for Singapore government datasets, vs generic data APIs that require manual schema mapping
Downloads datasets from data.gov.sg with support for multiple output formats (CSV, JSON, XML) and optional filtering/sampling to reduce payload size. Implements a download orchestration layer that handles format negotiation with the upstream API, applies client-side filtering predicates, and streams results to avoid memory exhaustion on large datasets.
Unique: Implements client-side filtering and format negotiation as MCP tools, allowing LLM agents to express data retrieval intents declaratively without writing download scripts; handles Singapore government data's specific format quirks and encoding issues
vs alternatives: Provides declarative, LLM-friendly dataset retrieval vs raw API calls, with built-in format conversion and filtering that reduces boilerplate code
Exposes data.gov.sg's dataset collections (curated groupings by theme, agency, or domain) as navigable MCP tools, enabling developers to explore datasets hierarchically rather than through flat search. Implements a collection tree abstraction that maps data.gov.sg's organizational structure and allows drilling down from high-level themes (e.g., 'Economy') to specific datasets.
Unique: Maps data.gov.sg's agency and thematic hierarchies as MCP tool trees, preserving organizational context that helps LLMs understand data provenance and relationships between datasets
vs alternatives: Provides hierarchical dataset discovery vs flat search-only interfaces, enabling context-aware exploration of Singapore government data by theme and agency
Tracks dataset update schedules and last-modified timestamps, enabling developers to monitor data freshness and trigger downstream processes when datasets are updated. Implements a metadata polling abstraction that queries data.gov.sg for update information and exposes it as queryable MCP tools, allowing agents to make freshness-aware decisions about data usage.
Unique: Exposes data.gov.sg's update metadata as MCP tools with freshness-aware semantics, enabling LLM agents to make intelligent caching and refresh decisions without manual timestamp management
vs alternatives: Provides declarative freshness tracking vs manual timestamp comparison, reducing boilerplate for data pipeline automation
Analyzes metadata across multiple datasets to identify potential correlations, shared dimensions, and relationships (e.g., datasets sharing geographic regions, time periods, or entity types). Implements a metadata graph abstraction that builds connections between datasets based on common fields, enabling developers to discover complementary datasets for joint analysis.
Unique: Builds a metadata relationship graph specific to Singapore government data, identifying correlations based on agency hierarchies, geographic divisions, and temporal alignment patterns
vs alternatives: Provides automated dataset correlation discovery vs manual catalog browsing, enabling LLM agents to autonomously identify complementary data sources
Retrieves metadata about data-publishing agencies, stewards, and contact information from data.gov.sg, enabling developers to understand data provenance and reach out to publishers for clarifications. Implements an agency directory abstraction that maps Singapore government organizational structure and exposes steward contact details and data governance policies.
Unique: Exposes Singapore government agency hierarchy and data steward information as MCP tools, enabling LLM agents to understand data provenance and governance context
vs alternatives: Provides structured agency and steward metadata vs unstructured web search, enabling programmatic data governance tracking
Retrieves download counts, view statistics, and popularity metrics for datasets from data.gov.sg, enabling developers to identify widely-used datasets and understand data consumption patterns. Implements a metrics aggregation layer that normalizes usage data across datasets and exposes it as queryable MCP tools.
Unique: Aggregates and exposes data.gov.sg's usage metrics as MCP tools, enabling LLM agents to make adoption-aware dataset selection decisions
vs alternatives: Provides programmatic access to dataset popularity metrics vs manual browsing of data.gov.sg website
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.
MCP Server for Singapore Government Open Data scores higher at 49/100 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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