| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification to expose KISTI (Korea Institute of Science and Technology Information) resources and APIs as standardized tools and resources that MCP-compatible clients (Claude, other LLMs) can discover and invoke. Uses MCP's JSON-RPC 2.0 message protocol with resource URIs and tool schemas to bridge KISTI's backend services into LLM tool-use workflows without requiring direct API integration code in client applications.
Unique: Provides MCP server wrapper specifically for KISTI (Korean scientific research infrastructure) — likely the only standardized MCP integration for Korean-language scientific databases, enabling non-Korean LLM applications to access KISTI resources through protocol-agnostic tool discovery
vs alternatives: Eliminates need for custom KISTI API integration code in each LLM application by standardizing access through MCP protocol, making KISTI resources available to any MCP-compatible client without reimplementation
Exposes KISTI's available tools, resources, and data endpoints through MCP's resource listing and tool schema mechanisms, allowing MCP clients to dynamically discover what KISTI capabilities are available without hardcoding tool definitions. Implements MCP's resources/ and tools/ endpoints to return JSON schemas describing KISTI search, retrieval, and metadata operations that clients can introspect and invoke with proper parameter validation.
Unique: Implements MCP resource/tool discovery pattern specifically for KISTI's heterogeneous research APIs, likely requiring custom schema mapping to normalize KISTI's native API definitions into MCP's standardized tool schema format
vs alternatives: Enables runtime tool discovery for KISTI resources rather than requiring hardcoded tool definitions in each client, reducing maintenance burden when KISTI APIs change
Translates MCP tool invocations into KISTI backend API calls, handling parameter mapping, request formatting, and response transformation. Implements the actual execution layer that receives structured search/query requests from MCP clients, constructs appropriate KISTI API calls (likely REST or SOAP), executes them against KISTI's scientific databases, and returns results in MCP-compatible JSON format for LLM consumption.
Unique: Implements query translation layer that maps generic MCP search tool invocations to KISTI's specific API conventions, likely handling Korean language queries and KISTI-specific metadata fields (e.g., Korean subject classifications) that differ from Western scientific databases
vs alternatives: Provides unified search interface to KISTI through MCP rather than requiring LLM applications to implement KISTI API integration directly, abstracting away KISTI's API complexity and authentication requirements
Implements the core MCP server message loop that receives JSON-RPC 2.0 requests from MCP clients, routes them to appropriate handler functions (resource discovery, tool invocation, etc.), executes handlers with proper error catching, and returns formatted JSON-RPC responses. Handles protocol-level concerns like request ID tracking, error serialization, timeout management, and graceful degradation when KISTI backend is unavailable.
Unique: Implements MCP protocol message loop with KISTI-specific error handling, likely including recovery strategies for KISTI API failures and timeout scenarios that are specific to Korean research infrastructure reliability patterns
vs alternatives: Provides protocol-compliant message routing that ensures MCP clients receive properly formatted responses regardless of KISTI backend state, improving reliability compared to direct KISTI API integration
Manages authentication credentials for KISTI backend services, handling credential storage, token refresh, and request signing. Implements credential injection into KISTI API calls while keeping credentials isolated from MCP client requests, preventing exposure of KISTI authentication details to downstream LLM applications. Likely uses environment variables, secure credential stores, or OAuth token management.
Unique: Implements credential isolation pattern where KISTI authentication is handled server-side, preventing MCP clients from needing direct KISTI credentials — likely using environment-based or vault-based credential injection specific to KISTI's authentication requirements
vs alternatives: Centralizes KISTI credential management in the MCP server rather than distributing credentials to each client, improving security and enabling credential rotation without client updates
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 kisti-mcp at 24/100. kisti-mcp 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