| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides TypeScript-first tool definition system that generates Model Context Protocol (MCP) compliant tool schemas with type safety. Uses TypeScript interfaces and decorators to define tool signatures, parameters, and return types that are automatically serialized into MCP tool definition format for agent consumption. Enables declarative tool registration with built-in validation of parameter schemas and tool metadata.
Unique: Provides TypeScript-native tool definition system that leverages type inference to automatically generate MCP-compliant schemas, eliminating manual JSON schema writing and ensuring compile-time type safety between tool definitions and agent invocations
vs alternatives: Offers stronger type safety than manual MCP tool definition because TypeScript types are enforced at definition time rather than runtime, reducing integration errors when agents invoke tools
Acts as a bridge layer between MCP tool definitions and ManyWe Agent runtime, handling tool discovery, parameter marshalling, and result serialization. Implements the MCP protocol handshake to register tools with the agent, manages tool invocation lifecycle, and handles error propagation from tool execution back to the agent. Supports both synchronous and asynchronous tool execution with timeout and retry semantics.
Unique: Implements MCP protocol adapter specifically optimized for ManyWe Agent's execution model, with built-in support for agent-specific context passing and result serialization patterns that other generic MCP implementations don't provide
vs alternatives: More seamless integration with ManyWe Agent than generic MCP implementations because it understands agent-specific execution contexts and can pass agent state directly to tools without serialization overhead
Automatically validates tool invocation parameters against TypeScript-defined schemas before execution, using JSON schema validation with support for complex types (unions, arrays, nested objects). Generates human-readable validation error messages that help agents understand parameter requirements. Supports custom validators and coercion rules for common type conversions (string-to-number, ISO date parsing, etc.).
Unique: Combines TypeScript compile-time type checking with runtime JSON schema validation, providing both development-time safety and production-time robustness that pure runtime validators or pure static typing alone cannot achieve
vs alternatives: More comprehensive than simple type checking because it validates at runtime against full JSON schemas including constraints, patterns, and custom rules that TypeScript's static types cannot express
Automatically extracts tool descriptions, parameter documentation, and usage examples from TypeScript definitions and JSDoc comments to generate human-readable tool documentation. Creates structured metadata (name, description, category, tags) that helps agents understand tool purpose and when to invoke them. Supports markdown formatting in descriptions for rich documentation rendering in agent interfaces.
Unique: Integrates JSDoc parsing with MCP tool schema generation to create bidirectional documentation where tool definitions are the source of truth for both code and documentation, eliminating documentation drift
vs alternatives: Reduces documentation maintenance burden compared to separate documentation systems because documentation lives in code and is automatically synchronized with tool definitions
Provides utilities for composing multiple tools into higher-level tool workflows, including sequential execution, conditional branching, and parallel tool invocation patterns. Implements tool composition as first-class abstractions that agents can invoke as single tools, abstracting away orchestration complexity. Supports passing outputs from one tool as inputs to subsequent tools with automatic type checking.
Unique: Treats tool composition as first-class abstractions that can be registered and invoked like regular tools, allowing agents to treat complex workflows as atomic operations without understanding underlying orchestration
vs alternatives: Simpler for agents to use than prompt-based orchestration because composition logic is explicit and type-checked rather than relying on agent reasoning about tool sequencing
Supports multiple versions of the same tool with automatic routing to appropriate implementation based on agent compatibility requirements. Tracks tool schema changes and provides migration utilities for updating tool definitions without breaking existing agent integrations. Enables gradual rollout of tool updates with version-specific parameter handling and deprecation warnings.
Unique: Implements semantic versioning for MCP tools with automatic routing and migration support, treating tool versions as first-class entities rather than requiring agents to manage version compatibility manually
vs alternatives: More robust than ad-hoc versioning because it enforces semantic versioning discipline and provides automated migration paths, reducing manual coordination overhead when updating tools
Manages execution context for tool invocations including agent identity, request metadata, user information, and request-scoped state. Provides context propagation through tool call chains so nested tools can access parent context without explicit parameter passing. Implements context isolation to prevent state leakage between concurrent tool invocations and supports context cleanup on tool completion.
Unique: Uses Node.js AsyncLocalStorage for automatic context propagation through async call chains without requiring explicit parameter passing, enabling clean tool signatures while maintaining full execution context
vs alternatives: Cleaner than explicit context parameters because context is automatically available to all tools in a call chain without polluting tool signatures, and more robust than global state because it's request-scoped and isolated
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 @manywe/mcp-tools at 23/100. @manywe/mcp-tools 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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