| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes web searches through Kagi's proprietary search API, returning ranked results with titles, snippets, and URLs. The MCP server translates search queries into Kagi API calls, handling authentication via API keys and formatting responses into structured JSON that Claude or other MCP clients can consume. Implements request batching and result pagination to support both single queries and multi-step search workflows.
Unique: Implements Kagi search as an MCP server, enabling Claude and other MCP clients to invoke web search as a native tool without custom API wrappers. Uses Kagi's privacy-focused search index (no user tracking) and integrates directly into the MCP protocol's function-calling mechanism, allowing declarative search composition within agent workflows.
vs alternatives: Offers privacy-first search integration for MCP clients (unlike Google/Bing APIs which track users), and provides direct Claude compatibility without requiring custom tool definitions or API orchestration code.
Wraps Kagi search functionality as an MCP-compliant tool that Claude and other MCP clients can discover and invoke through the Model Context Protocol. The server exposes search as a callable function with schema validation, parameter marshalling, and response serialization following MCP's tool definition standard. Handles tool discovery, schema advertisement, and request/response lifecycle management within the MCP message protocol.
Unique: Implements full MCP tool protocol compliance, including schema advertisement, parameter validation, and error handling within the MCP message lifecycle. Unlike generic API wrappers, this exposes search as a first-class MCP tool that Claude can discover and invoke with natural language, enabling seamless integration into agent reasoning loops.
vs alternatives: Provides native MCP integration (vs. custom tool definitions), enabling Claude to automatically invoke search without explicit prompt engineering, and allows tool discovery and composition within the MCP ecosystem.
Manages Kagi API authentication by storing and validating API keys, implementing request signing if required by Kagi's API, and enforcing rate limits to prevent quota exhaustion. The MCP server handles credential injection into outbound requests, token refresh if applicable, and graceful degradation when rate limits are exceeded. Implements exponential backoff for retries and tracks quota usage across multiple concurrent search requests.
Unique: Implements MCP-native credential handling where API keys are managed by the MCP server process, not by the client, ensuring keys are never exposed to Claude or other MCP clients. Uses environment-based configuration for secure key storage and implements client-side rate limiting with exponential backoff to prevent quota exhaustion.
vs alternatives: Separates credential management from client logic (vs. embedding keys in prompts or client code), and provides rate-limit protection without requiring manual quota tracking by the application.
Parses Kagi API responses and transforms raw search results into a standardized JSON format suitable for LLM consumption. Extracts relevant fields (title, snippet, URL, domain), sanitizes HTML entities and special characters, truncates long snippets to fit context windows, and structures results as an array with consistent schema. Handles edge cases like missing fields, malformed responses, and encoding issues.
Unique: Implements LLM-aware result formatting that prioritizes snippet clarity and token efficiency, including automatic truncation and domain extraction. Unlike generic API response passthrough, this normalizes Kagi's response schema into a format optimized for Claude's context window and reasoning capabilities.
vs alternatives: Provides LLM-optimized formatting (vs. raw API responses), with automatic snippet truncation and domain extraction, reducing the need for post-processing in agent code.
Enables agents to compose multiple sequential or parallel search queries, where results from one query can inform subsequent queries. The MCP server maintains request context across multiple tool invocations, allowing agents to refine searches based on intermediate results. Implements query deduplication to avoid redundant API calls and result caching within a single agent session to reduce API usage and latency.
Unique: Implements session-scoped result caching and query deduplication within the MCP server, allowing agents to perform multi-step research without redundant API calls. Unlike stateless search APIs, this maintains context across multiple tool invocations, enabling intelligent query refinement and result synthesis.
vs alternatives: Provides built-in caching and deduplication (vs. agents managing their own state), reducing API calls and latency for multi-step research workflows.
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 Kagi Search 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