kagi-ken-mcp vs toolhive
kagi-ken-mcp: This project is a lightweight Node.js-based Model Context Protocol (MCP) server that enables programmatic access to Kagi's search and summarization services using an existing Kagi session token. It offers an alternative to the official Kagi API, which currently requires invite-only API keys, making Kagi's powerful features available to LLMs and AI agents.; toolhive: ToolHive simplifies and secures the deployment of Model Context Protocol (MCP) servers, offering a comprehensive solution for managing AI toolchains in production. It provides a modular architecture with a Gateway, Registry Server, Runtime, and Portal for instant, secure, and scalable operations across various environments.
TL;DR
Access Kagi's search and summarizer programmatically without an official API key.
- You want MCP servers to run in isolated containers with scoped permissions and managed secrets, on Docker, Podman or Kubernetes.
- You want the same tooling from a laptop (desktop app or CLI) to a Kubernetes Operator.
- Your team needs a registry with provenance verification, an SSO-capable gateway and audit logging.
- You just want to run one MCP server: it needs Docker, Podman or Kubernetes and is heavier than starting the server directly.
- You want a single component: the Runtime, Registry, Gateway and Portal take time to understand, and parts live in separate repositories.
- You expect container isolation to replace reviewing what each server can reach.
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