The server implements calls of [API methods](https://help.kagi.com/kagi/api/overview.html): - fastgpt - enrich/web - enrich/news
Kagi Search MCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. The server implements calls of API methods: - fastgpt - enrich/web - enrich/news.
Once Kagi Search MCP is connected, these are the calls the assistant has available:
Resources — The server implements calls of API methods: - fastgpt - enrich/web - enrich/newsPrompts — The Prompts tool exposed by this serverTools — The server implements several tools: - ask_fastgpt to search web and find an answer - enrich_web to enrich model context with web content -Debugging — The Debugging tool exposed by this serverYou will need 2 environment variables: KAGI_API_KEY, UV_PUBLISH_TOKEN. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
@smithery/cli on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Among the search and retrieval options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Kagi Search MCP's toolset — Resources, Prompts, Tools and 1 more — is a fair guide to whether it matches your workflow. It is maintained by apridachin; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Resources | The server implements calls of [API methods](https://help.kagi.com/kagi/api/overview.html): - fastgpt - enrich/web - enrich/news |
| Prompts | The Prompts tool exposed by this server. |
| Tools | The server implements several tools: - ask_fastgpt to search web and find an answer - enrich_web to enrich model context with web content - enrich_news to enrich model context with latest news |
| Debugging | The Debugging tool exposed by this server. |
{
"mcpServers": {
"kagi-search": {
"command": "npx",
"args": ["-y", "@smithery/cli"],
"env": {
"KAGI_API_KEY": "your-value",
"UV_PUBLISH_TOKEN": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| KAGI_API_KEY | Credential the server authenticates with. | Yes |
| UV_PUBLISH_TOKEN | Credential the server authenticates with. | Yes |
The simplest web tool that matters — fetch any URL and get model-ready markdown back.
Industrial-strength web extraction — render, scrape, crawl and search entire sites into clean markdown.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Put 6,000+ pre-built scrapers at your assistant's fingertips through one MCP endpoint.