| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 25 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Reads HWPX (Open XML-based Korean word processor format) documents without requiring persistent state or the native Hangeul application, using pure Python XML parsing via python-hwpx library. Each tool invocation explicitly specifies the filename, enabling stateless operation across distributed MCP clients. The implementation parses OPC (Open Packaging Convention) package structure to extract document metadata, text content, and structural hierarchy (sections, paragraphs, tables) with character-level position tracking for context-aware retrieval.
Unique: Implements stateless document reading where every tool call includes explicit filename parameter, eliminating session state management and enabling safe concurrent access from multiple MCP clients. Uses OPC package parsing to directly access document.xml without requiring Hangeul application or binary format reverse-engineering.
vs alternatives: Eliminates Windows/Hangeul dependency that COM-based automation requires, while maintaining full cross-platform support (Windows/macOS/Linux) with pure Python implementation.
Converts HWPX documents to Markdown format with configurable output strategies (full document or chunked by section/paragraph). The conversion pipeline parses document structure, maps heading levels to Markdown syntax (#, ##, ###), preserves table formatting as Markdown tables, and optionally splits output into chunks based on character limits or structural boundaries. Supports both file-based input (filename) and payload-based input (base64-encoded HWPX or HTTPS URL), enabling both local and remote document processing.
Unique: Supports dual input modes (file path + payload-based base64/URL) with identical tool interface, enabling both local document processing and remote document fetching. Chunking respects document structure (section/paragraph boundaries) rather than naive character splitting, preserving semantic coherence for LLM processing.
vs alternatives: Faster than Hangeul application export because it uses direct XML parsing; more flexible than pandoc because it understands HWPX-specific structure (sections, custom styles) natively.
Creates a safe copy of HWPX documents with validation to ensure copy integrity. The copy operation reads source document, validates structure, writes to destination path, and verifies copy completeness. Useful for creating backups before destructive operations or generating document variants.
Unique: Validates copy integrity by comparing source and destination structure, ensuring safe document duplication before risky operations.
vs alternatives: More reliable than OS-level file copy because it validates HWPX structure integrity; enables safe document variant generation.
Creates new tables in HWPX documents with specified row and column counts. The table creation engine inserts a table element at specified paragraph index, initializes cells with empty content, and optionally applies basic formatting (header row styling). Tables are fully editable after creation via cell-level operations.
Unique: Creates properly structured HWPX tables with configurable dimensions and optional header styling, enabling data-driven document generation.
vs alternatives: More flexible than template-based tables because dimensions are configurable; more reliable than manual table insertion because it maintains HWPX structure integrity.
Modifies text content in table cells by table index, row, and column coordinates. The cell modification engine locates target cell, replaces content while preserving cell formatting, and supports batch operations for multiple cell updates. Enables data population in pre-created tables.
Unique: Provides cell-level table editing with coordinate-based access, enabling precise data population in tables.
vs alternatives: More granular than document-level editing because it operates at cell level; enables efficient data-driven table population.
Manages table cell merging and splitting operations to create complex table layouts. The merge operation combines adjacent cells horizontally or vertically, and the split operation divides merged cells back into individual cells. Supports both single-cell and range-based operations.
Unique: Supports both cell merging and splitting with directional control, enabling creation of complex table layouts programmatically.
vs alternatives: More flexible than fixed table structures because it allows dynamic layout adjustments; enables sophisticated report generation with merged headers and grouped data.
Applies basic formatting to tables including header row styling, border configuration, and cell alignment. The formatting engine applies predefined style templates to table elements, enabling consistent table appearance across documents.
Unique: Provides template-based table formatting that applies consistent styling across tables, enabling professional document appearance.
vs alternatives: More convenient than manual cell-by-cell formatting because it applies templates; ensures consistent table appearance across documents.
Applies formatting attributes (bold, italic, underline, color, font size, font family) to text ranges within paragraphs. The formatting engine locates target text, applies formatting attributes to underlying XML runs, and preserves existing formatting for non-targeted attributes. Supports both single-range and multi-range formatting operations.
Unique: Applies multiple formatting attributes simultaneously to text ranges while preserving existing formatting on non-targeted attributes.
vs alternatives: More flexible than paragraph-level formatting because it operates at text range level; enables selective emphasis within paragraphs.
+17 more capabilities
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 한글 mcp hwpx MCP Server at 35/100.
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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