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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes filesystem operations as standardized MCP Tools through a JSON-RPC 2.0 transport layer, allowing LLM clients to invoke file operations with structured request/response contracts. The server implements the MCP protocol primitives to register tool schemas, handle invocations, and return results in a format compatible with any MCP-aware client (Claude, custom agents, etc.). This abstraction decouples filesystem logic from transport concerns, enabling the same tool definitions to work across stdio, HTTP, or WebSocket transports.
Unique: Implements MCP's tool registration and invocation contract as a reference pattern, allowing any MCP-compatible client to discover and call filesystem operations without custom integration code. Uses TypeScript SDK's Server class to manage tool lifecycle and request routing.
vs alternatives: Provides protocol-level standardization that REST APIs or custom RPC layers don't offer, enabling zero-configuration integration with MCP-aware LLM clients like Claude.
Implements a security model where filesystem access is restricted to explicitly configured allowed paths (allowlist), preventing directory traversal and unauthorized file access. The server validates all incoming file paths against the allowlist before executing any operation, rejecting requests that attempt to access paths outside the permitted scope. Configuration is passed at server initialization time, allowing operators to define which directories or files LLM clients can interact with, with support for glob patterns or explicit path lists.
Unique: Uses a declarative allowlist model enforced at the tool invocation layer, validating paths before any filesystem operation executes. The reference implementation demonstrates this pattern clearly, making it easy for operators to understand and audit what access is granted.
vs alternatives: More explicit and auditable than capability-based security or role-based access control, making it easier for non-technical operators to understand what an LLM agent can and cannot access.
Provides a tool that reads file contents and returns them in the appropriate encoding (UTF-8 text or base64 binary), automatically detecting or accepting hints about file type. The implementation uses Node.js fs.readFile() with encoding parameters, returning text files as strings and binary files as base64-encoded strings to ensure JSON-RPC compatibility. The tool includes metadata about file size and encoding used, allowing clients to understand what they received.
Unique: Handles both text and binary files transparently by encoding binary data as base64, making it JSON-RPC-safe while preserving full file fidelity. The tool includes size metadata to help clients decide whether to process large files.
vs alternatives: More robust than simple text-only file readers because it gracefully handles binary files without corruption, and more transparent than opaque binary APIs because it explicitly encodes and reports what encoding is used.
Provides a tool to write content to files, creating them if they don't exist or overwriting them if they do, with support for both text and base64-encoded binary content. The implementation uses Node.js fs.writeFile() which provides atomic semantics on most filesystems (write to temp file, then rename), ensuring partial writes don't corrupt files. The tool validates the target path against the allowlist and returns confirmation with file size written.
Unique: Leverages Node.js fs.writeFile() atomic semantics (temp-file-then-rename pattern) to ensure writes are durable and don't leave partial files, which is critical for code generation workflows where incomplete files break builds.
vs alternatives: More reliable than stream-based writes for small-to-medium files because atomic semantics prevent partial writes, and more transparent than opaque file APIs because it reports exact bytes written and supports both text and binary.
Provides a tool to list files and subdirectories within a specified path, with optional recursive traversal to show the full directory tree. The implementation uses Node.js fs.readdirSync() or fs.promises.readdir() with recursive option, returning structured metadata for each entry (name, type, size, modification time). Clients can filter results by file type or pattern, and the tool respects the allowlist to prevent listing unauthorized directories.
Unique: Combines directory listing with optional recursive traversal and structured metadata output, allowing agents to build a mental model of project structure without multiple round-trips. The reference implementation shows how to safely traverse directories while respecting allowlist boundaries.
vs alternatives: More informative than simple ls-style output because it includes file sizes and modification times, and more efficient than requiring separate stat calls for each file because metadata is returned in a single operation.
Provides a tool to delete files or directories, with an optional recursive flag to remove non-empty directories and their contents. The implementation uses Node.js fs.rmSync() or fs.promises.rm() with recursive option, validating the target path against the allowlist before deletion. The tool returns confirmation of what was deleted and the number of files/directories removed.
Unique: Implements deletion as a controlled tool with explicit allowlist enforcement, preventing accidental or malicious removal of files outside the permitted scope. The reference implementation demonstrates safe patterns for exposing destructive operations to LLM agents.
vs alternatives: Safer than unrestricted shell access because the allowlist prevents deletion of system files, and more transparent than opaque deletion APIs because it reports exactly what was removed.
Provides a tool to move files from one location to another or rename them, with validation that both source and destination paths pass the allowlist. The implementation uses Node.js fs.renameSync() or fs.promises.rename(), which is atomic on most filesystems. The tool handles conflicts by either failing if the destination exists or optionally overwriting, depending on configuration.
Unique: Validates both source and destination against the allowlist, preventing moves that would escape the permitted scope. Uses atomic rename semantics to ensure moves are durable and don't leave partial state.
vs alternatives: More secure than unrestricted file operations because both paths are validated, and more reliable than manual copy-then-delete patterns because rename is atomic.
Provides a tool to retrieve file or directory metadata (size, modification time, permissions, type) without reading the full file content, using Node.js fs.statSync() or fs.promises.stat(). This is efficient for large files where only metadata is needed. The tool returns structured information about file type, size in bytes, creation/modification/access times, and permission bits.
Unique: Separates metadata retrieval from content reading, allowing agents to make intelligent decisions about which files to process without the overhead of reading large files. The reference implementation demonstrates this as a distinct tool rather than bundling it with read operations.
vs alternatives: More efficient than reading file content just to check size or modification time, and more transparent than opaque stat APIs because it returns all available metadata in a structured format.
+2 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 Filesystem at 22/100.
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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