| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a Model Context Protocol server that exposes HTTP fetching as standardized tools via stdin/stdout communication. The server registers tool handlers with the MCP SDK, validates incoming requests using Zod schemas, and returns responses formatted according to MCP specification. This enables any MCP-compatible client (Claude, custom agents, etc.) to invoke web fetching without custom HTTP client implementation.
Unique: Implements MCP server pattern with stdio-based communication and Zod schema validation, enabling seamless integration with MCP-aware clients without requiring HTTP server infrastructure or custom protocol negotiation
vs alternatives: Simpler deployment than REST API servers (no port management, firewall rules) and more standardized than custom tool protocols, but less flexible than HTTP APIs for cross-language integration
Uses JSDOM to parse HTML documents into a virtual DOM, then extracts text content while removing HTML markup, scripts, and styling. The Fetcher class instantiates a JSDOM window, traverses the DOM tree, and returns cleaned text. This approach preserves text structure and readability while stripping all HTML artifacts, making content suitable for LLM processing without markup noise.
Unique: Leverages JSDOM's full DOM implementation rather than regex or simple HTML stripping, enabling accurate text extraction from complex nested structures and handling of edge cases like nested tags and entity encoding
vs alternatives: More accurate than regex-based HTML stripping (handles nested tags, entities correctly) but slower than lightweight parsers like cheerio; better for content extraction than for performance-critical scenarios
Integrates TurndownService to convert HTML documents into Markdown format while preserving semantic structure (headings, lists, links, emphasis). The service maps HTML elements to Markdown equivalents and applies configurable rules for handling edge cases. This enables LLMs to work with structured content that retains formatting cues without raw HTML complexity.
Unique: Uses TurndownService's rule-based HTML-to-Markdown mapping rather than simple regex replacement, enabling semantic preservation of document structure (headings, lists, links, emphasis) and handling of edge cases through configurable conversion rules
vs alternatives: Preserves more semantic structure than plain text extraction, making output more useful for LLMs; more reliable than regex-based converters but slower than simple text extraction
Fetches content from a URL, parses the response as JSON using native JSON.parse(), and validates the structure using Zod schemas. If parsing fails, returns an error response. This capability enables agents to reliably consume JSON APIs and validate response schemas before passing data downstream.
Unique: Combines native JSON.parse() with Zod schema validation in a single tool, enabling both parsing and structural validation without requiring separate validation steps or custom error handling in client code
vs alternatives: More robust than raw JSON.parse() (includes validation) but adds latency vs simple parsing; simpler than full OpenAPI client generation but less feature-rich
Fetches HTTP content from a URL using the native fetch API and returns the raw HTML response body. Supports optional custom HTTP headers (User-Agent, Authorization, etc.) to handle authentication, content negotiation, and server-specific requirements. This is the foundational capability that other transformations (text, Markdown, JSON) build upon.
Unique: Exposes native fetch API through MCP tool interface with support for custom headers, enabling agents to handle authentication, content negotiation, and server-specific requirements without custom HTTP client code
vs alternatives: Simpler than full HTTP client libraries (no dependency bloat) but less feature-rich than axios or node-fetch wrappers; native fetch is faster than alternatives but offers fewer convenience methods
Uses Zod schemas to validate all incoming tool requests before processing. Each tool (fetch_html, fetch_json, fetch_txt, fetch_markdown) has a corresponding Zod schema that validates URL format, header structure, and required fields. Invalid requests are rejected with structured error messages before reaching the fetcher logic, preventing malformed requests from consuming resources.
Unique: Implements Zod-based request validation at the MCP server layer before tool execution, providing type-safe input handling and structured error messages without requiring validation logic in individual tool implementations
vs alternatives: More robust than manual validation (catches edge cases) and provides better error messages than simple type checking; adds minimal latency vs runtime validation
Registers four tools (fetch_html, fetch_json, fetch_txt, fetch_markdown) with the MCP SDK and binds request handlers to each tool. The server implements the MCP tool listing protocol (returning tool schemas) and tool calling protocol (executing tools and returning results). This enables MCP clients to discover available tools and invoke them with proper request/response formatting.
Unique: Implements MCP tool registration pattern with static schema definitions and handler binding, enabling clients to discover and invoke tools through a standardized protocol without custom negotiation or discovery mechanisms
vs alternatives: More standardized than custom tool protocols but less flexible than dynamic tool registration; simpler than REST API servers but requires MCP-aware clients
Catches exceptions during fetch operations (network errors, timeouts, parsing failures) and returns structured error responses through the MCP protocol. Errors include descriptive messages indicating the failure type (network error, invalid URL, parsing failure, etc.) without exposing internal stack traces. This enables clients to handle failures gracefully and retry or fallback appropriately.
Unique: Implements error handling at the MCP server layer with descriptive error messages and no stack trace exposure, enabling clients to handle failures gracefully while maintaining security and debuggability
vs alternatives: More user-friendly than raw exception propagation but less detailed than structured error codes; simpler than full retry logic but requires client-side retry implementation
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 fetch-mcp at 27/100. fetch-mcp leads on adoption and 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
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