| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Renders interactive Vega-Lite visualizations within Angular components by consuming structured chart specifications emitted from Data360 MCP tools. The component receives Vega-Lite JSON specifications (axis definitions, data bindings, mark types, encodings) and delegates rendering to the Vega-Lite library, enabling declarative data visualization without custom D3 or Canvas code. Integrates with MCP protocol to receive tool outputs as standardized chart payloads.
Unique: Purpose-built Angular component specifically designed to consume Data360 MCP tool outputs, eliminating the need for developers to manually parse MCP responses and configure Vega-Lite charts separately. Tightly coupled to MCP protocol and World Bank Data360 tool ecosystem rather than a generic Vega-Lite wrapper.
vs alternatives: More specialized than generic Vega-Lite Angular wrappers (like ngx-vega) because it understands MCP tool output structure and Data360 semantics, reducing integration boilerplate for World Bank data workflows.
Provides a pre-configured Angular card component that automatically maps MCP tool output fields (title, description, data payload, metadata) to visual card elements (header, body, footer). The component accepts an MCP tool response object and extracts chart specification, labels, and contextual metadata, rendering them in a cohesive card layout. Handles schema translation between MCP response format and Vega-Lite input requirements.
Unique: Implements automatic schema translation between MCP tool response format and Angular component inputs, reducing manual field mapping. The card component is specifically designed for Data360 MCP tools rather than generic visualization containers.
vs alternatives: Eliminates boilerplate compared to building custom card layouts with generic Vega-Lite components; developers don't need to manually extract and bind MCP response fields to template variables.
Manages user interactions with rendered Vega-Lite charts (click, hover, selection) and exposes them as Angular event emitters or RxJS observables. The component captures Vega-Lite interaction events (selections, brushing, clicking marks) and propagates them to parent components, enabling drill-down, filtering, or secondary analysis workflows. Implements two-way data binding between chart state and Angular component state.
Unique: Bridges Vega-Lite's native interaction model with Angular's event system and RxJS observables, enabling reactive data flows. Specifically designed for MCP tool outputs where interactions may trigger secondary MCP tool calls or data transformations.
vs alternatives: More tightly integrated with Angular's change detection and reactive patterns than generic Vega-Lite wrappers; enables seamless composition with other Angular services and state management libraries.
Provides Angular service bindings to consume MCP tool outputs directly, handling protocol-level details (message serialization, response parsing, error handling). The service abstracts MCP client communication, allowing components to request Data360 tool execution and receive results as typed Angular observables. Implements request/response correlation and timeout handling for asynchronous MCP calls.
Unique: Provides Angular-idiomatic service layer for MCP protocol communication, integrating with Angular's dependency injection and RxJS reactive patterns. Handles MCP-specific concerns (message serialization, request correlation) transparently.
vs alternatives: More integrated with Angular ecosystem than raw MCP client libraries; developers use familiar Angular services and observables rather than learning MCP protocol details.
Automatically resizes rendered Vega-Lite charts when the parent container dimensions change, using Angular's ResizeObserver or viewport change detection. The component listens to container size changes and updates the Vega-Lite specification's width/height properties, triggering re-render. Supports responsive breakpoints for mobile, tablet, and desktop layouts.
Unique: Implements responsive chart resizing using Angular's lifecycle hooks and ResizeObserver, automatically adapting Vega-Lite specifications without manual dimension management. Tailored for Data360 dashboards that need to work across device sizes.
vs alternatives: More automatic than manual resize handling; developers don't need to implement custom resize listeners or manage chart dimensions explicitly.
Provides methods to export rendered Vega-Lite charts as PNG, SVG, or JSON specification files. The component leverages Vega-Lite's built-in export capabilities (canvas rendering for raster formats, SVG serialization) and wraps them in Angular methods that trigger browser downloads. Supports batch export of multiple charts in a dashboard.
Unique: Wraps Vega-Lite's native export functionality in Angular methods, providing seamless integration with Angular's file download patterns. Specifically designed for Data360 analysis workflows where users need to export results.
vs alternatives: More integrated with Angular than raw Vega-Lite export APIs; provides Angular-idiomatic download triggers and error handling.
Implements accessibility features including ARIA labels, semantic HTML structure, keyboard navigation support, and high-contrast mode compatibility. Ensures chart components are navigable via keyboard, provide text alternatives for visual data, and work with screen readers. Leverages Vega-Lite's built-in accessibility features (e.g., data table export) and enhances them with Angular-specific accessibility patterns like focus management and ARIA live regions for dynamic updates.
Unique: Combines Vega-Lite's built-in accessibility features with Angular-specific patterns (focus management, ARIA live regions) for comprehensive chart accessibility rather than relying solely on Vega-Lite's defaults.
vs alternatives: More comprehensive accessibility support than generic Vega-Lite Angular wrappers, with explicit ARIA labeling and keyboard navigation patterns tailored to data visualization
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 @data360/mcp-ui-angular at 23/100. @data360/mcp-ui-angular 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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