| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides TypeScript type definitions and runtime validation guards for Data360 MCP (Model Context Protocol) tool JSON payloads. Uses zod or similar schema validation libraries to enforce structural correctness at both compile-time (via TypeScript) and runtime (via guard functions), ensuring tool definitions conform to MCP specification before transmission to LLM clients.
Unique: Purpose-built for Data360 MCP tool payloads specifically, rather than generic MCP validation — encodes World Bank data tool conventions and constraints directly into type definitions and guards
vs alternatives: More specialized than generic JSON schema validators because it understands Data360-specific tool semantics and provides pre-built guards for common World Bank data operations
Exports TypeScript guard functions (type predicates) that perform runtime validation of MCP tool payloads at execution time. These guards check payload structure, required fields, and type constraints using discriminated unions and exhaustive checking patterns, enabling safe narrowing of tool objects before passing them to LLM clients or executing tool logic.
Unique: Provides discriminated union-based guards that leverage TypeScript's type narrowing to create type-safe execution paths, rather than generic schema validation that requires manual type assertions
vs alternatives: More ergonomic than manual instanceof checks or schema validation because guards integrate directly with TypeScript's type system, enabling IDE autocomplete after validation
Encodes the complete schema for Data360 MCP tool payloads as TypeScript interfaces and types, including World Bank data tool conventions such as parameter types, response formats, and metadata structures. These schemas serve as the source of truth for tool definition structure across Data360 deployments, enabling consistent tool implementation and validation.
Unique: Encodes World Bank data tool conventions directly into TypeScript types rather than generic MCP schemas, capturing domain-specific constraints like parameter naming, response pagination, and metadata requirements
vs alternatives: More domain-aware than generic MCP type libraries because it reflects actual Data360 tool patterns and World Bank data API conventions, reducing friction for teams building within the ecosystem
Enables TypeScript's type inference engine to validate tool payload construction at compile-time by providing strongly-typed interfaces and builder patterns. As developers construct tool payloads, TypeScript provides real-time feedback on missing or incorrect fields, preventing invalid tool definitions from being compiled or deployed.
Unique: Leverages TypeScript's structural typing and strict mode to provide compile-time validation of tool payloads, catching errors before runtime rather than relying on schema validation
vs alternatives: More developer-friendly than runtime schema validation because errors appear in the IDE during development, with autocomplete guidance, rather than as runtime exceptions in production
Validates that tool payloads conform to the Model Context Protocol specification, including required fields, field types, and protocol-level constraints. This ensures that tools built with @data360/tool-types can be safely transmitted to and executed by MCP-compliant clients and servers without protocol violations.
Unique: Validates against the MCP specification directly rather than generic JSON schema, ensuring tools are compatible with the specific protocol version and constraints that LLM clients expect
vs alternatives: More precise than generic schema validation because it understands MCP-specific constraints like tool naming conventions, parameter requirements, and response format expectations
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/tool-types at 21/100. @data360/tool-types 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
+1 more capabilities