| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements MCP tool registration that exposes HTTP GET/POST fetching as a callable tool through the Model Context Protocol's JSON-RPC transport layer. The server registers a 'fetch' tool with input schema validation, handles HTTP requests via Python's urllib or requests library, and returns structured responses that conform to MCP tool result primitives, enabling LLM clients to invoke web fetching as a first-class capability without direct HTTP knowledge.
Unique: Official MCP reference implementation that demonstrates tool registration patterns using the Python SDK's Server class and tool decorator, showing how to map HTTP operations to MCP's standardized tool invocation model with schema-based input validation
vs alternatives: More lightweight and protocol-compliant than custom HTTP wrappers because it integrates directly with MCP's transport layer, allowing any MCP client to invoke fetching without custom integration code
Transforms fetched HTML content into Markdown format optimized for LLM processing using a conversion library (likely html2text or similar). The server parses HTML structure, preserves semantic meaning (headings, lists, links, emphasis), strips unnecessary styling and scripts, and outputs clean Markdown that reduces token consumption and improves LLM comprehension compared to raw HTML. This conversion happens server-side before returning results to the MCP client.
Unique: Integrates HTML-to-Markdown conversion as a built-in post-processing step within the MCP tool response pipeline, ensuring all fetched content is automatically normalized to LLM-friendly format without requiring client-side conversion logic
vs alternatives: More efficient than returning raw HTML to clients because conversion happens once server-side and reduces downstream token consumption; simpler than clients implementing their own HTML parsing and Markdown generation
Implements robots.txt parsing and compliance validation before fetching URLs, checking the User-Agent against disallowed paths and crawl-delay directives defined in the target domain's robots.txt file. The server fetches and caches robots.txt entries, evaluates requested URLs against allow/disallow rules, and either permits or blocks the fetch based on compliance. This ensures the MCP server respects web scraping conventions and legal/ethical boundaries without requiring clients to implement their own robots.txt logic.
Unique: Embeds robots.txt compliance as a mandatory pre-flight check in the MCP tool invocation pipeline, preventing disallowed fetches at the server level rather than relying on client-side enforcement or post-hoc filtering
vs alternatives: More reliable than client-side robots.txt checking because it enforces compliance at the server boundary; simpler than clients implementing their own robots.txt parsing and caching logic
Defines the 'fetch' tool's input schema using JSON Schema format (with required fields like 'url' and optional fields like 'method', 'headers', 'body') and validates incoming MCP tool call requests against this schema before processing. The server uses the MCP SDK's tool registration mechanism to declare the schema, and the framework automatically validates inputs, returning structured validation errors if the request doesn't match the schema. This ensures type safety and prevents malformed requests from reaching the HTTP fetching logic.
Unique: Leverages MCP SDK's built-in tool registration and schema validation framework, which automatically validates inputs against the declared schema without requiring manual validation code in the tool handler
vs alternatives: More maintainable than manual input validation because schema is declarative and validated by the framework; provides better error messages and client documentation compared to ad-hoc validation logic
Manages the MCP server's startup, shutdown, and transport initialization using the Python SDK's Server class and async context managers. The server initializes the MCP protocol handler, registers tools (fetch, etc.) during startup, establishes stdio or network transport for client communication, and gracefully shuts down resources on exit. This lifecycle management ensures the server is ready to receive MCP requests and properly cleans up connections when the client disconnects or the server terminates.
Unique: Uses MCP SDK's async Server class with context manager pattern, enabling clean resource management and automatic tool registration without manual protocol handling or transport setup code
vs alternatives: Simpler than implementing MCP protocol from scratch because the SDK handles JSON-RPC serialization, transport negotiation, and message routing; more reliable than custom server implementations because it follows MCP specification patterns
Catches HTTP errors (4xx, 5xx, network timeouts, connection failures) and maps them to structured MCP error responses with descriptive messages. The server distinguishes between client errors (404 Not Found, 403 Forbidden), server errors (500 Internal Server Error), and network errors (timeout, DNS failure), returning appropriate error codes and messages that clients can interpret. This ensures fetch failures are communicated clearly without crashing the server or leaving the MCP connection in an inconsistent state.
Unique: Maps HTTP and network errors to MCP error response primitives, ensuring fetch failures are communicated through the MCP protocol rather than causing server crashes or protocol violations
vs alternatives: More robust than returning raw HTTP errors because it wraps errors in MCP-compliant responses; better for client error handling than silent failures or generic exceptions
Allows clients to specify custom HTTP headers (including User-Agent, Authorization, Accept, etc.) in the fetch tool request, enabling access to APIs that require specific headers or authentication. The server passes these headers through to the HTTP request, allowing clients to override the default User-Agent (which might be blocked by some sites) or add authentication tokens. This flexibility enables the fetch tool to work with a wider range of web services and APIs without requiring server-side configuration changes.
Unique: Exposes HTTP header customization as a first-class parameter in the MCP tool schema, allowing clients to specify headers per-request without requiring server-side configuration or separate authentication mechanisms
vs alternatives: More flexible than hardcoded headers because clients can customize headers per-request; simpler than implementing separate authentication mechanisms (OAuth, API key management) because it delegates header handling to clients
Implements a maximum response body size limit (typically 1-10 MB) to prevent memory exhaustion from fetching extremely large files or responses. When a response exceeds the limit, the server truncates the body and returns a truncation indicator, allowing clients to know that the full content was not retrieved. This protects the server from out-of-memory errors and ensures fetch operations complete in reasonable time, though it may lose information from large documents.
Unique: Implements server-side response size limiting as a safety mechanism, preventing clients from accidentally triggering memory exhaustion through large fetch requests without requiring client-side size validation
vs alternatives: More protective than relying on clients to check response sizes because the limit is enforced at the server boundary; simpler than implementing streaming responses because truncation is transparent to clients
+1 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.
Fetch MCP Server scores higher at 59/100 vs Supabase at 42/100.
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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