Supercharge AI tools with Kagi MCP: fast google web search API, powerful ai summarizer, and seamless ai summary tool int
by kagisearch·Updated Apr 13, 2026Fetch and extract information from websites automatically
Example: Research competitor pricing, scrape product reviews, monitor news mentions
Automate 5-10 hours/week of manual web research
Track website changes, new content, price updates
Example: Monitor competitor blog for new posts, track stock availability, watch for pricing changes
Stay informed without manual checking, never miss important updates
Extract structured data from multiple websites
Example: Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data
Build datasets 100x faster than manual copying
Interact with services that don't offer APIs
Example: Check form submissions, validate website functionality, test user flows
Automate interactions with any website, even without API
Fetch (Web Content & YouTube Transcripts)
Fetch is a web scraping tool that extracts web content and YouTube transcripts, converting HTML to Markdown with accurat
DeepWiki
DeepWiki converts deepwiki.com pages into clean Markdown, with fast, secure extraction—perfect as a PDF text, page, or i
Fetch with Images
Fetch with Images integrates web scraping and image compresser com tools to scrape any website and optimize images effic
Airbnb
Integrate with Airbnb for effortless vacation rental search and detailed property info—no API keys needed. Ideal for ren
ZoomEye MCP Server
ZoomEye MCP Server — lightweight ZoomEye client to query the ZoomEye API for fast internet asset search and discovery.
We evaluated Kagi Search against two servers with overlapping tools; this profile had the clearer scope statement.
Strong directory entry: Kagi Search surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Kagi Search has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Kagi Search reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We evaluated Kagi Search against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend Kagi Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Kagi Search has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Useful MCP listing: Kagi Search is the kind of server we cite when onboarding engineers to host + tool permissions.
Kagi Search is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: Kagi Search surfaces stars and publisher context so we could sanity-check maintenance before adopting.
showing 1-10 of 64
Share your MCP server with the developer community
Select a client to integrate this server.
Claude Desktop configuration
{
"mcpServers": {
"kagi": {
"command": "uvx",
"args": ["kagimcp"],
"env": {
"KAGI_API_KEY": "YOUR_API_KEY_HERE",
"KAGI_SUMMARIZER_ENGINE": "YOUR_ENGINE_CHOICE_HERE"
}
}
}
}Connects your AI tools to Kagi's search API and summarizer to find real-time web information and generate summaries from web content, articles, or videos.
Kagi Search is an official MCP server published by kagisearch that provides AI assistants with tools and capabilities via the Model Context Protocol. Supercharge AI tools with Kagi MCP: fast google web search API, powerful ai summarizer, and seamless ai summary tool int It is categorized under search web.
You can install Kagi Search in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
Before anything, unless you are just using non-search tools, ensure you have access to the search API. It is currently in closed beta and available upon request. Please reach out to support@kagi.com for an invite.
Install uv first.
MacOS/Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Alternatively, you can install Kagi for Claude Desktop via Smithery:
npx -y @smithery/cli install kagimcp --client claude
// claude_desktop_config.json
// Can find location through:
// Hamburger Menu -> File -> Settings -> Developer -> Edit Config
{
"mcpServers": {
"kagi": {
"command": "uvx",
"args": ["kagimcp"],
"env": {
"KAGI_API_KEY": "YOUR_API_KEY_HERE",
"KAGI_SUMMARIZER_ENGINE": "YOUR_ENGINE_CHOICE_HERE" // Defaults to "cecil" engine if env var not present
}
}
}
}
Add the Kagi mcp server with the following command (setting summarizer engine optional):
claude mcp add kagi -e KAGI_API_KEY="YOUR_API_KEY_HERE" KAGI_SUMMARIZER_ENGINE="YOUR_ENGINE_CHOICE_HERE" -- uvx kagimcp
Now claude code can use the Kagi mcp server. However, claude code comes with its own web search functionality by default, which may conflict with Kagi. You can disable claude's web search functionality with the following in your claude code settings file (~/.claude/settings.json):
{
"permissions": {
"deny": [
"WebSearch"
]
}
}
e.g. "Who was time's 2024 person of the year?" for search, or "summarize this video: https://www.youtube.com/watch?v=jNQXAC9IVRw" for summarizer.
Run:
npx @modelcontextprotocol/inspector uvx kagimcp
git clone https://github.com/kagisearch/kagimcp.git
Install uv first.
MacOS/Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Then install MCP server dependencies:
cd kagimcp
# Create virtual environment and activate it
uv venv
source .venv/bin/activate # MacOS/Linux
# OR
.venv/Scripts/activate # Windows
# Install dependencies
uv sync
# `pip install mcp[cli]` if you haven't
mcp install /ABSOLUTE/PATH/TO/PARENT/FOLDER/kagimcp/src/kagimcp/server.py -v "KAGI_API_KEY=API_KEY_HERE"
# claude_desktop_config.json
# Can find location through:
# Hamburger Menu -> File -> Settings -> Developer -> Edit Config
{
"mcpServers": {
"kagi": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/PARENT/FOLDER/kagimcp",
"run",
"kagimcp"
],
"env": {
"KAGI_API_KEY": "YOUR_API_KEY_HERE",
"KAGI_SUMMARIZER_ENGINE": "YOUR_ENGINE_CHOICE_HERE" // Defaults to "cecil" engine if env var not present
}
}
}
}
e.g. "Who was time's 2024 person of the year?" for search, or "summarize this video: https://www.youtube.com/watch?v=jNQXAC9IVRw" for summarizer.
Run:
# If mcp cli installed (`pip install mcp[cli]`)
mcp dev /ABSOLUTE/PATH/TO/PARENT/FOLDER/kagimcp/src/kagimcp/server.py
# If not
npx @modelcontextprotocol/inspector \
uv \
--directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/kagimcp \
run \
kagimcp
Then access MCP Inspector at http://localhost:5173. You may need to add your Kagi API key in the environment variables in the inspector under KAGI_API_KEY.
FASTMCP_LOG_LEVEL environment variable (e.g. FASTMCP_LOG_LEVEL="ERROR")
KAGI_SUMMARIZER_ENGINE environment variable (e.g. KAGI_SUMMARIZER_ENGINE="daphne")
Time estimate: 20-40 minutes including configuration and testing
Prerequisites
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.
Protocols
Compatibility
✓ Use when
Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.
✗ Avoid when
Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.