| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes web searches against DuckDuckGo's HTML interface (not API-dependent) and returns formatted results with titles, URLs, and snippets cleaned for LLM consumption. The search tool implements query parameter handling with configurable max_results (default 10) and applies post-processing to remove ads and clean redirect URLs before returning structured text output. Built on FastMCP framework's @mcp.tool() decorator pattern for seamless MCP protocol integration.
Unique: Uses DuckDuckGo's HTML interface scraping instead of requiring API keys or paid search services, combined with LLM-specific result post-processing (ad removal, URL cleaning) rather than returning raw search results. Implements MCP protocol binding via FastMCP framework, making it a drop-in tool for MCP-compatible clients without additional orchestration.
vs alternatives: Eliminates API key management and cost overhead compared to Google Custom Search or Bing Search API, while providing privacy-first search without tracking; faster integration than building custom web search from scratch due to MCP protocol standardization.
Retrieves raw HTML from specified URLs and parses it into cleaned, LLM-friendly text content using HTML parsing libraries. The fetch_content tool accepts a URL parameter, handles HTTP requests with error management, strips HTML markup, removes boilerplate (navigation, ads, scripts), and returns structured text suitable for LLM context injection. Implements rate limiting (20 requests/minute) and comprehensive error handling for network failures, invalid URLs, and parsing exceptions.
Unique: Combines HTTP fetching with HTML parsing and boilerplate removal in a single MCP tool, specifically optimized for LLM consumption (removes ads, scripts, navigation) rather than returning raw HTML. Integrates directly into MCP protocol flow, allowing LLMs to chain search → fetch → analyze without external tool orchestration.
vs alternatives: Simpler than building custom web scraping pipelines; more LLM-optimized than generic HTML-to-text converters by removing ads and boilerplate; integrated into MCP protocol unlike standalone libraries like Selenium or Puppeteer.
Implements token-bucket style rate limiting with separate quotas for search (30 req/min) and content fetching (20 req/min) operations. The rate limiter tracks request timestamps and enforces delays or rejections when quotas are exceeded, preventing service abuse and DuckDuckGo overload. Built into the tool execution pipeline before external requests are made, with error responses returned to the MCP client when limits are hit.
Unique: Implements dual-quota rate limiting (30 req/min search, 20 req/min content) at the MCP tool execution layer rather than at HTTP client level, providing tool-specific throttling that reflects actual service impact. Integrated into FastMCP framework's tool decorator pattern, making limits transparent to MCP clients without additional configuration.
vs alternatives: More granular than generic HTTP rate limiters (separate quotas per tool); simpler than distributed rate limiting systems (no Redis/external state needed); integrated into MCP protocol layer vs requiring separate middleware.
Implements the Model Context Protocol (MCP) specification using the FastMCP framework, exposing search and content fetching as standardized MCP tools with schema validation, error handling, and protocol-compliant request/response serialization. The server initializes as a FastMCP instance with identifier 'ddg-search', decorates tool methods with @mcp.tool(), and handles MCP client communication including tool discovery, invocation, and result formatting. Supports multiple deployment modes (Smithery, Python package, Docker) with standardized MCP configuration.
Unique: Uses FastMCP framework to abstract MCP protocol complexity, allowing tool definitions via simple Python decorators (@mcp.tool()) rather than manual protocol handling. Provides standardized tool discovery and invocation without custom client integration code, supporting multiple deployment modes (Smithery, pip, Docker) with identical MCP interface.
vs alternatives: Simpler than building custom MCP servers from scratch (FastMCP handles protocol details); more standardized than REST API wrappers (MCP protocol ensures client compatibility); supports multiple deployment modes vs single-deployment-model tools.
Provides three deployment pathways: Smithery (simplified MCP server registry installation), Python pip package installation, and Docker containerization. Each deployment method maintains identical MCP tool interface and functionality while accommodating different infrastructure preferences. Smithery integration enables one-click installation in Claude Desktop; pip allows local Python environment installation; Docker enables containerized deployment with environment isolation. Configuration is standardized across all deployment modes via environment variables and MCP configuration files.
Unique: Supports three distinct deployment pathways (Smithery registry, pip package, Docker container) with unified MCP interface, allowing users to choose infrastructure based on preference without code changes. Smithery integration provides one-click Claude Desktop installation, eliminating manual configuration for non-technical users.
vs alternatives: More flexible than single-deployment-model tools (supports Smithery, pip, Docker); simpler than custom deployment scripts (standardized across modes); Smithery integration reduces friction vs manual MCP server setup.
Implements multi-layer error handling covering network failures (connection timeouts, DNS resolution), invalid inputs (malformed URLs, empty queries), parsing failures (corrupted HTML, encoding issues), and rate limit violations. Each error type is caught, logged, and returned to the MCP client with descriptive error messages rather than crashing the server. Includes fallback behaviors such as partial result return on parsing failures and clear error codes for client-side retry logic.
Unique: Implements comprehensive exception handling at the MCP tool layer, catching and converting Python exceptions into MCP-compliant error responses rather than propagating crashes. Provides descriptive error messages for network, parsing, and validation failures, enabling client-side retry logic and fallback strategies.
vs alternatives: More robust than tools without error handling (prevents server crashes); more informative than generic HTTP error codes (specific error types for client logic); integrated into MCP protocol vs requiring separate error handling middleware.
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.
DuckDuckGo 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
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