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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Performs semantic search across 1000+ public repositories and documentation sites through the Ref API, returning intelligently filtered results that minimize irrelevant context. The system tracks session-based search trajectories to avoid redundant queries and implements result ranking to surface the most relevant documentation snippets, reducing token consumption compared to unfiltered full-document retrieval.
Unique: Implements session-based search trajectory tracking (index.ts 537-544) to maintain stateful search context across multiple requests, combined with client-specific response formatting (DeepResearchShape for OpenAI vs plain text for MCP) to optimize both token efficiency and client compatibility. Uses Ref API's pre-indexed corpus of 1000+ repos rather than requiring local indexing.
vs alternatives: More token-efficient than RAG systems requiring full document loading because it returns filtered snippets with source attribution, and faster than web search because it queries a pre-indexed documentation corpus rather than crawling in real-time.
Fetches and extracts content from specific documentation URLs through the Ref API, returning formatted content optimized for the detected client type. Implements client detection logic (index.ts 23-37, 394-422) to return DeepResearchShape JSON for OpenAI clients or plain text for standard MCP clients, enabling seamless integration across different AI agent architectures.
Unique: Implements dynamic client detection and response formatting (createServerInstance function, index.ts 61-212) that adapts output structure based on detected client type without requiring explicit configuration. Uses Ref API's server-side HTML parsing rather than client-side extraction, reducing agent complexity.
vs alternatives: More reliable than generic web scraping because it uses Ref API's documentation-aware parsing, and more flexible than hardcoded response formats because it auto-detects client type and returns appropriate structure (JSON for OpenAI, text for MCP).
Deploys as an MCP server supporting both stdio (local npm package) and HTTP (remote service) transports, with HTTP transport implementing session management through transports and sessionClientInfo objects (index.ts 376-536, 537-544). Enables stateful interactions across multiple requests in HTTP mode while maintaining compatibility with local stdio execution, allowing the same codebase to serve both embedded and remote deployment scenarios.
Unique: Implements transport abstraction (StdioServerTransport vs StreamableHTTPServerTransport) with unified tool handling logic, enabling single codebase deployment across local and remote scenarios. HTTP transport includes session tracking via transports and sessionClientInfo objects for stateful multi-request interactions, while stdio remains stateless.
vs alternatives: More flexible than single-transport MCP servers because it supports both local and remote deployment without code duplication, and more stateful than typical HTTP APIs because it maintains per-client session context for search trajectory tracking.
Implements a three-tier authentication resolution system (getAuthHeaders function, index.ts 221-242) that prioritizes runtime configuration over environment variables, enabling dynamic API key switching without server restart. Supports both standard REF_API_KEY and early-access REF_ALPHA authentication paths, constructing appropriate X-Ref-Api-Key or X-Ref-Alpha headers and including session identifiers for HTTP transport requests.
Unique: Implements priority-based resolution (runtime config > environment variables > alpha access) allowing dynamic API key switching via HTTP parameters without server restart, combined with session identifier injection for stateful API interactions. Supports both standard and alpha authentication paths.
vs alternatives: More flexible than static environment-variable-only authentication because it allows runtime override, and more secure than hardcoded keys because it supports environment-based and runtime-configured credentials with session isolation.
Dynamically detects client type through multiple mechanisms (User-Agent headers, explicit hints, client registry) and adapts tool response formats accordingly. OpenAI clients receive DeepResearchShape JSON objects with structured title/content/source fields, while standard MCP clients receive plain text markdown, enabling seamless integration across heterogeneous AI agent architectures without requiring client-specific configuration.
Unique: Implements client detection and response formatting within createServerInstance (index.ts 61-212) using dynamic tool name and response format configuration based on detected client type, enabling single MCP server to serve both OpenAI and standard MCP clients transparently without requiring separate server instances.
vs alternatives: More flexible than single-format MCP servers because it adapts response structure based on client type, and more seamless than requiring explicit client configuration because detection is automatic via User-Agent and headers.
Tracks search history and query patterns within HTTP sessions to avoid redundant searches and inform result ranking. The session-based trajectory system (index.ts 537-544) maintains per-client search context, enabling the system to understand search intent progression and filter results based on previous queries, reducing token waste from repeated documentation lookups and improving result relevance over multiple agent interactions.
Unique: Implements session-based search trajectory tracking (transports and sessionClientInfo objects) that maintains per-client search history and uses it to filter redundant results and inform ranking, enabling context-aware search across multiple agent interactions without requiring explicit context passing.
vs alternatives: More context-aware than stateless search APIs because it tracks search history within sessions, and more efficient than full RAG systems because it uses trajectory information to avoid redundant retrievals rather than storing all results.
Provides multiple deployment methods (npm package, Docker container, HTTP server, Smithery platform) with unified environment-variable-based configuration. Supports TRANSPORT_TYPE selection, API key configuration via REF_API_KEY/REF_ALPHA, and HTTP port customization, enabling flexible deployment across development, staging, and production environments without code changes.
Unique: Supports four distinct deployment methods (npm, Docker, HTTP, Smithery) from single codebase using environment-based configuration, enabling teams to choose deployment strategy without code changes. Unified configuration approach across all deployment methods.
vs alternatives: More flexible than single-deployment-method tools because it supports npm, Docker, HTTP, and Smithery without code duplication, and more portable than hardcoded configuration because environment variables enable seamless environment switching.
Defines two core MCP tools (search_documentation and read_url) with client-specific naming conventions and schema validation. The tool definitions include input schemas with required/optional parameters, output descriptions, and client-specific naming adaptations (e.g., different tool names for OpenAI vs standard MCP clients), enabling proper tool discovery and invocation across heterogeneous MCP clients.
Unique: Implements client-specific tool naming and schema adaptation within CallToolRequestSchema handler (index.ts 65-93), allowing same tool to be exposed with different names to different clients (e.g., search_documentation for OpenAI, ref_search for standard MCP) without duplicating tool logic.
vs alternatives: More flexible than static tool definitions because it adapts tool names based on client type, and more discoverable than implicit tools because it provides explicit MCP schema definitions for proper client integration.
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 Ref at 25/100.
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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
+1 more capabilities