| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a Model Context Protocol server that wraps the Pandoc document conversion library, enabling AI assistants and MCP clients to invoke format transformations through standardized tool-call semantics. The server registers a single convert-contents tool that accepts source content or file paths, validates input/output format compatibility, and delegates conversion to pypandoc, which internally shells out to the native Pandoc binary. This architecture decouples the MCP communication layer from the underlying conversion engine, allowing Claude Desktop and other MCP-compatible clients to transparently access Pandoc's 30+ format support without direct binary invocation.
Unique: Exposes Pandoc's full format library through MCP's standardized tool-call protocol, allowing AI assistants to invoke conversions as first-class operations without requiring users to manage CLI invocations or external scripts. Distinguishes between basic formats (returned as strings in responses) and advanced formats (requiring filesystem operations), enabling efficient in-conversation conversions while supporting complex file-based workflows.
vs alternatives: Unlike standalone Pandoc CLI or Python pypandoc bindings, mcp-pandoc integrates directly into Claude's tool ecosystem, enabling conversational format decisions and multi-step document workflows without context switching or manual file management.
The convert-contents tool accepts two mutually-exclusive input modes: direct content strings (for in-memory conversions) or complete file paths (for filesystem-based operations). The tool validates that exactly one input source is provided, then routes to the appropriate pypandoc method — either `convert_text()` for string inputs or `convert_file()` for file paths. This dual-mode design enables both lightweight conversational conversions (e.g., 'convert this markdown snippet to HTML') and heavyweight batch operations (e.g., 'convert all DOCX files in /documents to PDF'), without requiring separate tools or complex parameter negotiation.
Unique: Implements a single tool with two distinct execution paths (content-string vs file-path) rather than separate tools, reducing cognitive load for users while maintaining clean separation of concerns internally. The validation logic ensures mutual exclusivity, preventing ambiguous or conflicting input specifications.
vs alternatives: More flexible than tools that support only file inputs (requiring users to save snippets to disk) or only string inputs (limiting batch operations), while simpler than multi-tool approaches that duplicate conversion logic across separate endpoints.
The server implements a two-tier output strategy based on format classification: basic formats (markdown, HTML, plain text) are converted via pypandoc and returned directly as strings in the MCP response, enabling zero-latency in-conversation results; advanced formats (PDF, DOCX, RST, LaTeX, EPUB) require an explicit output_file parameter and are written to the filesystem, since these binary or complex formats cannot be serialized into MCP text responses. This routing logic is enforced at the tool parameter level — advanced formats will reject requests without an output_file path, preventing silent failures or incomplete conversions.
Unique: Explicitly separates basic and advanced formats with different output mechanisms (in-response strings vs filesystem writes), optimizing for the common case of lightweight text conversions while supporting complex binary formats. This two-tier design is enforced at the tool schema level, preventing invalid parameter combinations before execution.
vs alternatives: More efficient than tools that always write to disk (adding latency for simple conversions) or always return strings (failing on binary formats), while clearer than tools that silently choose output modes based on format, which can surprise users.
The server delegates all format conversion logic to the pypandoc Python library, which wraps the native Pandoc binary and provides a Pythonic API (`convert_text()`, `convert_file()` methods). This abstraction layer shields the MCP server from direct binary invocation, error handling, and version compatibility concerns. pypandoc internally manages Pandoc subprocess spawning, argument marshaling, and stdout/stderr capture, allowing the server to focus on MCP protocol compliance and tool parameter validation rather than low-level process management.
Unique: Relies on pypandoc as a thin abstraction layer over Pandoc, avoiding custom subprocess orchestration and format-specific parsing logic. This design prioritizes simplicity and maintainability over performance, accepting the overhead of Python subprocess spawning in exchange for leveraging Pandoc's comprehensive format support.
vs alternatives: Simpler than custom Pandoc wrappers that reimplement subprocess management and error handling, while more flexible than hardcoded format converters that support only a subset of Pandoc's formats. Trades some performance for code simplicity and format breadth.
The server implements MCP's tool-listing and tool-execution handlers by registering a convert-contents tool with a detailed JSON schema that defines required parameters (contents or input_file, input_format, output_format, and conditionally output_file for advanced formats), parameter types, and descriptions. When an MCP client invokes the tool, the server validates incoming parameters against this schema before delegating to pypandoc, ensuring type safety and preventing invalid format combinations (e.g., requesting PDF output without an output_file path). This schema-driven approach enables MCP clients like Claude to provide autocomplete, parameter hints, and client-side validation before tool invocation.
Unique: Implements MCP's tool-registration pattern with a detailed JSON schema that enforces parameter constraints at the protocol level, enabling client-side hints and validation. The schema explicitly distinguishes between basic and advanced formats, with conditional output_file requirements, making invalid parameter combinations detectable before execution.
vs alternatives: More discoverable and user-friendly than tools without schema documentation, while more flexible than tools with hardcoded parameter validation that cannot adapt to new formats. Leverages MCP's standard tool-listing mechanism, making the tool accessible to any MCP-compatible client without custom integration code.
The server exposes a single convert-contents tool that handles all format conversion workflows, rather than separate tools for each format pair or conversion mode. This stateless design means each tool invocation is independent — no session state, no conversion history, no format caching — and the server maintains no internal state between requests. The tool accepts all necessary parameters (input, format, output path) in a single call, enabling straightforward MCP client integration and horizontal scaling (multiple server instances can handle requests without coordination).
Unique: Consolidates all format conversions into a single, stateless tool rather than format-specific or mode-specific endpoints, prioritizing simplicity and horizontal scalability over advanced features like caching or multi-step pipelines. This design aligns with MCP's philosophy of simple, composable tools.
vs alternatives: Simpler to integrate and scale than stateful tools that maintain conversion history or session context, while less feature-rich than tools with built-in caching or pipeline support. Trades advanced capabilities for straightforward, predictable behavior.
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 Pandoc at 24/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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