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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes search queries against a SearXNG instance through the Model Context Protocol, translating MCP tool-call schemas into HTTP requests to SearXNG's REST API and marshaling results back as structured JSON. The implementation wraps SearXNG's `/search` endpoint with MCP's standardized tool-calling interface, enabling LLM agents to invoke searches without direct HTTP knowledge.
Unique: Bridges SearXNG (a privacy-respecting metasearch engine) with MCP protocol, enabling LLM agents to use decentralized search without relying on commercial search APIs. Unlike direct HTTP integration, MCP standardization allows any MCP-compatible client (Claude, custom agents) to use the same interface.
vs alternatives: Provides privacy-first search integration for MCP agents without vendor lock-in to OpenAI/Google APIs, though with lower result quality than commercial search engines due to SearXNG's aggregation model
Translates MCP tool-call parameters into SearXNG-compatible query parameters, handling schema validation, parameter normalization, and optional argument handling. The server maintains a mapping layer between MCP's standardized tool schema and SearXNG's query API, including support for filters like language, category, and time range if the SearXNG instance exposes them.
Unique: Implements a declarative parameter mapping layer that abstracts SearXNG's query API behind MCP's tool schema, allowing clients to remain agnostic to SearXNG's specific parameter names and formats while maintaining type safety.
vs alternatives: More maintainable than hardcoding SearXNG parameters directly in agent prompts, and more flexible than generic HTTP wrappers because it validates parameters before execution
Receives heterogeneous search results from SearXNG's aggregated engines (Google, Bing, DuckDuckGo, etc.) and normalizes them into a consistent JSON schema with fields like title, URL, snippet, and source engine. The normalization layer handles varying result formats from different search engines and presents a unified interface to MCP clients.
Unique: Normalizes results from SearXNG's multi-engine aggregation into a single schema, preserving source attribution so clients can trace which engine provided each result — useful for privacy audits and result quality analysis.
vs alternatives: More transparent than opaque search APIs because it exposes which engine returned each result, enabling agents to make informed decisions about result trustworthiness
Accepts configuration parameters (SearXNG instance URL, optional authentication credentials) to connect to a specific SearXNG deployment, with optional auto-discovery of instance capabilities via SearXNG's `/config` endpoint. The server can detect available search engines, supported languages, and categories from the target instance, adapting its tool schema dynamically.
Unique: Supports dynamic discovery of SearXNG instance capabilities via the `/config` endpoint, allowing the MCP server to adapt its tool schema to match the actual engines and languages available on the target instance rather than assuming a fixed configuration.
vs alternatives: More flexible than hardcoded SearXNG configurations because it auto-detects capabilities, enabling the same MCP server to work with different SearXNG deployments without code changes
Implements the Model Context Protocol specification for tool servers, exposing search capabilities as standardized MCP tools with JSON Schema definitions. The server registers tool definitions with required and optional parameters, handles MCP tool-call requests, and returns results in MCP's expected format, enabling seamless integration with any MCP-compatible client.
Unique: Fully implements MCP protocol specification, exposing SearXNG as a standardized tool that any MCP-compatible client can discover and invoke without custom integration code. This enables SearXNG to be composed with other MCP tools in a unified agent architecture.
vs alternatives: More interoperable than custom HTTP wrappers because it uses a standard protocol that multiple clients already support, reducing integration friction
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 SearXNG at 20/100.
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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